In our project, we are interested on the county level cases data. From the country level data, we would be taking the three different time series summary including confirmed, deaths and recovered covid cases across the world. They are named as
· time_series_covid19_recovered_global.csv
· time_series_covid19_deaths_global.csv
· time_series_covid19_confirmed_global.csv
As a data scientist your role is to extract the times series records of confirmed, recovered and death related information for the country INDIA from the above url.
As you know, Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) capable of learning order dependence in sequence prediction problems.
After preprocessing of the data, you are re
After preprocessing the raw data, you are assigned to build a LSTM model to predict the below:
Confirmed cases
Recovered cases
Death cases
Mortality Rate
#importing relevent packages
import pandas as pd
import numpy as np
import seaborn as sns
# For plotting
import matplotlib.pyplot as plt
import plotly as py
import plotly.graph_objs as go
import seaborn as sns
import plotly.express as px
# pip install jupyter-dash
confirmed = pd.read_csv('time_series_covid19_confirmed_global.csv')
deaths = pd.read_csv('time_series_covid19_deaths_global.csv')
recovered = pd.read_csv('time_series_covid19_recovered_global.csv')
confirmed.head()
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 5/3/22 | 5/4/22 | 5/5/22 | 5/6/22 | 5/7/22 | 5/8/22 | 5/9/22 | 5/10/22 | 5/11/22 | 5/12/22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 178901 | 178901 | 178905 | 178919 | 178922 | 178981 | 179010 | 179017 | 179131 | 179169 |
| 1 | NaN | Albania | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 275191 | 275211 | 275266 | 275310 | 275341 | 275366 | 275372 | 275416 | 275440 | 275485 |
| 2 | NaN | Algeria | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 265782 | 265782 | 265786 | 265791 | 265794 | 265798 | 265800 | 265804 | 265806 | 265808 |
| 3 | NaN | Andorra | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 41349 | 41717 | 41717 | 41717 | 41717 | 41717 | 41717 | 41717 | 41717 | 42156 |
| 4 | NaN | Angola | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 |
5 rows × 846 columns
deaths.head()
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 5/3/22 | 5/4/22 | 5/5/22 | 5/6/22 | 5/7/22 | 5/8/22 | 5/9/22 | 5/10/22 | 5/11/22 | 5/12/22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 7683 | 7683 | 7684 | 7684 | 7684 | 7684 | 7685 | 7685 | 7686 | 7686 |
| 1 | NaN | Albania | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 3496 | 3496 | 3496 | 3496 | 3496 | 3497 | 3497 | 3497 | 3497 | 3497 |
| 2 | NaN | Algeria | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6875 | 6875 | 6875 | 6875 | 6875 | 6875 | 6875 | 6875 | 6875 | 6875 |
| 3 | NaN | Andorra | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 153 | 153 | 153 | 153 | 153 | 153 | 153 | 153 | 153 | 153 |
| 4 | NaN | Angola | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1900 | 1900 | 1900 | 1900 | 1900 | 1900 | 1900 | 1900 | 1900 | 1900 |
5 rows × 846 columns
recovered.head()
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 5/3/22 | 5/4/22 | 5/5/22 | 5/6/22 | 5/7/22 | 5/8/22 | 5/9/22 | 5/10/22 | 5/11/22 | 5/12/22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | NaN | Albania | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | NaN | Algeria | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | NaN | Andorra | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | NaN | Angola | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 846 columns
confirmed.info()
print("-----")
deaths.info()
print("-----")
recovered.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 284 entries, 0 to 283 Columns: 846 entries, Province/State to 5/12/22 dtypes: float64(2), int64(842), object(2) memory usage: 1.8+ MB ----- <class 'pandas.core.frame.DataFrame'> RangeIndex: 284 entries, 0 to 283 Columns: 846 entries, Province/State to 5/12/22 dtypes: float64(2), int64(842), object(2) memory usage: 1.8+ MB ----- <class 'pandas.core.frame.DataFrame'> RangeIndex: 269 entries, 0 to 268 Columns: 846 entries, Province/State to 5/12/22 dtypes: float64(2), int64(842), object(2) memory usage: 1.7+ MB
confirmed.shape
(284, 846)
print("The total number of confirmed in the world : ",confirmed["3/1/21"].sum())
print("The total number of deaths in the world : ",deaths["3/1/21"].sum())
print("The total number of recovered in the world : ",recovered["3/1/21"].sum())
The total number of confirmed in the world : 114874619 The total number of deaths in the world : 2614584 The total number of recovered in the world : 64633387
# 15 countries with the highest number of confirmed
confirmed.sort_values(["1/19/21"], ascending = False)[["Country/Region","3/1/21"]].head(15)
| Country/Region | 3/1/21 | |
|---|---|---|
| 256 | US | 28797863 |
| 148 | India | 11124527 |
| 31 | Brazil | 10598476 |
| 218 | Russia | 4209850 |
| 273 | United Kingdom | 4182009 |
| 131 | France | 3736390 |
| 154 | Italy | 2938371 |
| 255 | Turkey | 2711479 |
| 238 | Spain | 3204531 |
| 135 | Germany | 2447068 |
| 93 | Colombia | 2255260 |
| 7 | Argentina | 2112023 |
| 184 | Mexico | 2089281 |
| 214 | Poland | 1711772 |
| 236 | South Africa | 1513959 |
x = confirmed.sort_values(["1/19/21"], ascending = False)[["Country/Region","3/1/21"]].head(15)
fig = px.bar(x, x = "3/1/21", y = "Country/Region")
fig.show()
# 15 countries with the highest number of confirmed
confirmed.sort_values(["3/1/21"], ascending = False)[["Country/Region","3/1/21"]].head(15)
| Country/Region | 3/1/21 | |
|---|---|---|
| 256 | US | 28797863 |
| 148 | India | 11124527 |
| 31 | Brazil | 10598476 |
| 218 | Russia | 4209850 |
| 273 | United Kingdom | 4182009 |
| 131 | France | 3736390 |
| 238 | Spain | 3204531 |
| 154 | Italy | 2938371 |
| 255 | Turkey | 2711479 |
| 135 | Germany | 2447068 |
| 93 | Colombia | 2255260 |
| 7 | Argentina | 2112023 |
| 184 | Mexico | 2089281 |
| 214 | Poland | 1711772 |
| 150 | Iran | 1639679 |
x = recovered.sort_values(["3/1/21"], ascending = False)[["Country/Region","3/1/21"]].head(15)
fig = px.bar(x, x = "Country/Region", y = "3/1/21")
fig.show()
# Top countries with the highest number of deahts
deaths.sort_values(["3/1/21"], ascending = False)[["Country/Region","3/1/21"]].head(5)
| Country/Region | 3/1/21 | |
|---|---|---|
| 256 | US | 512904 |
| 31 | Brazil | 256007 |
| 184 | Mexico | 186152 |
| 148 | India | 157248 |
| 212 | Peru | 123365 |
x = deaths.sort_values(["3/1/21"], ascending = False)[["Country/Region","3/1/21"]].head(15)
fig = px.bar(x, x = "Country/Region", y = "3/1/21")
fig.show()
# Dates a list of cloumn names in each list
x = recovered.iloc[:,4:]
dates = x.columns
dates
Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',
'1/28/20', '1/29/20', '1/30/20', '1/31/20',
...
'5/3/22', '5/4/22', '5/5/22', '5/6/22', '5/7/22', '5/8/22', '5/9/22',
'5/10/22', '5/11/22', '5/12/22'],
dtype='object', length=842)
# The country to be focussed, which in our case is India
country = 'India'
# Holds collective count of all available countries on a particular date
global_case_count=[]
global_death_count=[]
global_recovery_count=[]
country_confirmed=[]
country_death=[]
country_recovered=[]
for i in dates:
global_case_count.append(confirmed[i].sum())
global_death_count.append(deaths[i].sum())
global_recovery_count.append(recovered[i].sum())
#There can be multiple entries for a country, by state/province
country_confirmed.append(confirmed[confirmed["Country/Region"] == country][i].sum())
country_death.append(deaths[deaths["Country/Region"] == "India"][i].sum())
country_recovered.append(recovered[recovered["Country/Region"] == country][i].sum())
#Daily increase in numbers
def daily_increment(data):
temp=[]
for i in range(len(data)):
if i==0:
temp.append(data[0])
else:
temp.append(data[i]-data[i-1])
return temp
global_increment_confirmed=daily_increment(india_confirmed)
global_increment_death=daily_increment(india_death)
global_increment_recovered=daily_increment(india_recovered)
# Visualization
from plotly.offline import init_notebook_mode, iplot, plot
days = np.arange(0,200)
def daily_visualization(recovered, deaths, confirmed, country):
trace1 = go.Scatter(x = days,
y = confirmed[0:200],
mode = "lines",
name = "{} confirmed cases".format(country))
trace2 = go.Scatter(x = days,
y = deaths[0:200],
mode = "lines",
name = "{} Death cases".format(country))
trace3 = go.Scatter(x = days,
y = recovered[0:200],
mode = "lines",
name = "{} recovered cases".format(country))
data = [trace1, trace2, trace3]
layout=dict(title="{} 200 Covid-19 Trend Table ".format(country))
fig=dict(data=data,layout=layout)
iplot(fig)
# Global trend on covid
daily_visualization(global_increment_confirmed, global_increment_death, global_increment_recovered, "Global")
# Covid trends in India
daily_visualization(country_confirmed, country_death, country_recovered, country)
# Countries' total number of cases for 364 days
time=np.arange(0,364)
trace1=go.Scatter(x=time,
y=turkiye_vaka,
mode="lines",
name="India")
trace2=go.Scatter(x=time,
y=cin_vaka,
mode="lines",
name="Çin")
trace3=go.Scatter(x=time,
y=italya_vaka,
mode="lines",
name="İtalya")
trace4=go.Scatter(x=time,
y=ispanya_vaka,
mode="lines",
name="İspanya")
trace5=go.Scatter(x=time,
y=fransa_vaka,
mode="lines",
name="Fransa")
trace6=go.Scatter(x=time,
y=almanya_vaka,
mode="lines",
name="Almanya")
trace7=go.Scatter(x=time,
y=rusya_vaka,
mode="lines",
name="Rusya")
trace8=go.Scatter(x=time,
y=uk_vaka,
mode="lines",
name="United Kingdom")
# data=[trace1,trace2,trace3,trace4,trace5,trace6,trace7,trace8]
data=[trace1]
layout=dict(title="Covid-19 Vaka")
fig=dict(data=data,layout=layout)
iplot(fig)
time=np.arange(0,364)
trace1=go.Scatter(x=time,
y=turkiye_olum,
mode="lines",
name="Türkiye")
trace2=go.Scatter(x=time,
y=cin_olum,
mode="lines",
name="Çin")
trace3=go.Scatter(x=time,
y=italya_olum,
mode="lines",
name="İtalya")
trace4=go.Scatter(x=time,
y=ispanya_olum,
mode="lines",
name="İspanya")
trace5=go.Scatter(x=time,
y=fransa_olum,
mode="lines",
name="Fransa")
trace6=go.Scatter(x=time,
y=almanya_olum,
mode="lines",
name="Almanya")
trace7=go.Scatter(x=time,
y=rusya_olum,
mode="lines",
name="Rusya")
trace8=go.Scatter(x=time,
y=uk_olum,
mode="lines",
name="United Kingdom")
data=[trace1,trace2,trace3,trace4,trace5,trace6,trace7,trace8]
layout=dict(title="Covid-19 Ölüm")
fig=dict(data=data,layout=layout)
iplot(fig)
confirmed.style.set_properties(subset=['ad_description'], **{'width-max': '100px'})
confirmed.head(10).style.background_gradient(cmap='cool')
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | 2/1/20 | 2/2/20 | 2/3/20 | 2/4/20 | 2/5/20 | 2/6/20 | 2/7/20 | 2/8/20 | 2/9/20 | 2/10/20 | 2/11/20 | 2/12/20 | 2/13/20 | 2/14/20 | 2/15/20 | 2/16/20 | 2/17/20 | 2/18/20 | 2/19/20 | 2/20/20 | 2/21/20 | 2/22/20 | 2/23/20 | 2/24/20 | 2/25/20 | 2/26/20 | 2/27/20 | 2/28/20 | 2/29/20 | 3/1/20 | 3/2/20 | 3/3/20 | 3/4/20 | 3/5/20 | 3/6/20 | 3/7/20 | 3/8/20 | 3/9/20 | 3/10/20 | 3/11/20 | 3/12/20 | 3/13/20 | 3/14/20 | 3/15/20 | 3/16/20 | 3/17/20 | 3/18/20 | 3/19/20 | 3/20/20 | 3/21/20 | 3/22/20 | 3/23/20 | 3/24/20 | 3/25/20 | 3/26/20 | 3/27/20 | 3/28/20 | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | 4/8/20 | 4/9/20 | 4/10/20 | 4/11/20 | 4/12/20 | 4/13/20 | 4/14/20 | 4/15/20 | 4/16/20 | 4/17/20 | 4/18/20 | 4/19/20 | 4/20/20 | 4/21/20 | 4/22/20 | 4/23/20 | 4/24/20 | 4/25/20 | 4/26/20 | 4/27/20 | 4/28/20 | 4/29/20 | 4/30/20 | 5/1/20 | 5/2/20 | 5/3/20 | 5/4/20 | 5/5/20 | 5/6/20 | 5/7/20 | 5/8/20 | 5/9/20 | 5/10/20 | 5/11/20 | 5/12/20 | 5/13/20 | 5/14/20 | 5/15/20 | 5/16/20 | 5/17/20 | 5/18/20 | 5/19/20 | 5/20/20 | 5/21/20 | 5/22/20 | 5/23/20 | 5/24/20 | 5/25/20 | 5/26/20 | 5/27/20 | 5/28/20 | 5/29/20 | 5/30/20 | 5/31/20 | 6/1/20 | 6/2/20 | 6/3/20 | 6/4/20 | 6/5/20 | 6/6/20 | 6/7/20 | 6/8/20 | 6/9/20 | 6/10/20 | 6/11/20 | 6/12/20 | 6/13/20 | 6/14/20 | 6/15/20 | 6/16/20 | 6/17/20 | 6/18/20 | 6/19/20 | 6/20/20 | 6/21/20 | 6/22/20 | 6/23/20 | 6/24/20 | 6/25/20 | 6/26/20 | 6/27/20 | 6/28/20 | 6/29/20 | 6/30/20 | 7/1/20 | 7/2/20 | 7/3/20 | 7/4/20 | 7/5/20 | 7/6/20 | 7/7/20 | 7/8/20 | 7/9/20 | 7/10/20 | 7/11/20 | 7/12/20 | 7/13/20 | 7/14/20 | 7/15/20 | 7/16/20 | 7/17/20 | 7/18/20 | 7/19/20 | 7/20/20 | 7/21/20 | 7/22/20 | 7/23/20 | 7/24/20 | 7/25/20 | 7/26/20 | 7/27/20 | 7/28/20 | 7/29/20 | 7/30/20 | 7/31/20 | 8/1/20 | 8/2/20 | 8/3/20 | 8/4/20 | 8/5/20 | 8/6/20 | 8/7/20 | 8/8/20 | 8/9/20 | 8/10/20 | 8/11/20 | 8/12/20 | 8/13/20 | 8/14/20 | 8/15/20 | 8/16/20 | 8/17/20 | 8/18/20 | 8/19/20 | 8/20/20 | 8/21/20 | 8/22/20 | 8/23/20 | 8/24/20 | 8/25/20 | 8/26/20 | 8/27/20 | 8/28/20 | 8/29/20 | 8/30/20 | 8/31/20 | 9/1/20 | 9/2/20 | 9/3/20 | 9/4/20 | 9/5/20 | 9/6/20 | 9/7/20 | 9/8/20 | 9/9/20 | 9/10/20 | 9/11/20 | 9/12/20 | 9/13/20 | 9/14/20 | 9/15/20 | 9/16/20 | 9/17/20 | 9/18/20 | 9/19/20 | 9/20/20 | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | 10/1/20 | 10/2/20 | 10/3/20 | 10/4/20 | 10/5/20 | 10/6/20 | 10/7/20 | 10/8/20 | 10/9/20 | 10/10/20 | 10/11/20 | 10/12/20 | 10/13/20 | 10/14/20 | 10/15/20 | 10/16/20 | 10/17/20 | 10/18/20 | 10/19/20 | 10/20/20 | 10/21/20 | 10/22/20 | 10/23/20 | 10/24/20 | 10/25/20 | 10/26/20 | 10/27/20 | 10/28/20 | 10/29/20 | 10/30/20 | 10/31/20 | 11/1/20 | 11/2/20 | 11/3/20 | 11/4/20 | 11/5/20 | 11/6/20 | 11/7/20 | 11/8/20 | 11/9/20 | 11/10/20 | 11/11/20 | 11/12/20 | 11/13/20 | 11/14/20 | 11/15/20 | 11/16/20 | 11/17/20 | 11/18/20 | 11/19/20 | 11/20/20 | 11/21/20 | 11/22/20 | 11/23/20 | 11/24/20 | 11/25/20 | 11/26/20 | 11/27/20 | 11/28/20 | 11/29/20 | 11/30/20 | 12/1/20 | 12/2/20 | 12/3/20 | 12/4/20 | 12/5/20 | 12/6/20 | 12/7/20 | 12/8/20 | 12/9/20 | 12/10/20 | 12/11/20 | 12/12/20 | 12/13/20 | 12/14/20 | 12/15/20 | 12/16/20 | 12/17/20 | 12/18/20 | 12/19/20 | 12/20/20 | 12/21/20 | 12/22/20 | 12/23/20 | 12/24/20 | 12/25/20 | 12/26/20 | 12/27/20 | 12/28/20 | 12/29/20 | 12/30/20 | 12/31/20 | 1/1/21 | 1/2/21 | 1/3/21 | 1/4/21 | 1/5/21 | 1/6/21 | 1/7/21 | 1/8/21 | 1/9/21 | 1/10/21 | 1/11/21 | 1/12/21 | 1/13/21 | 1/14/21 | 1/15/21 | 1/16/21 | 1/17/21 | 1/18/21 | 1/19/21 | 1/20/21 | 1/21/21 | 1/22/21 | 1/23/21 | 1/24/21 | 1/25/21 | 1/26/21 | 1/27/21 | 1/28/21 | 1/29/21 | 1/30/21 | 1/31/21 | 2/1/21 | 2/2/21 | 2/3/21 | 2/4/21 | 2/5/21 | 2/6/21 | 2/7/21 | 2/8/21 | 2/9/21 | 2/10/21 | 2/11/21 | 2/12/21 | 2/13/21 | 2/14/21 | 2/15/21 | 2/16/21 | 2/17/21 | 2/18/21 | 2/19/21 | 2/20/21 | 2/21/21 | 2/22/21 | 2/23/21 | 2/24/21 | 2/25/21 | 2/26/21 | 2/27/21 | 2/28/21 | 3/1/21 | 3/2/21 | 3/3/21 | 3/4/21 | 3/5/21 | 3/6/21 | 3/7/21 | 3/8/21 | 3/9/21 | 3/10/21 | 3/11/21 | 3/12/21 | 3/13/21 | 3/14/21 | 3/15/21 | 3/16/21 | 3/17/21 | 3/18/21 | 3/19/21 | 3/20/21 | 3/21/21 | 3/22/21 | 3/23/21 | 3/24/21 | 3/25/21 | 3/26/21 | 3/27/21 | 3/28/21 | 3/29/21 | 3/30/21 | 3/31/21 | 4/1/21 | 4/2/21 | 4/3/21 | 4/4/21 | 4/5/21 | 4/6/21 | 4/7/21 | 4/8/21 | 4/9/21 | 4/10/21 | 4/11/21 | 4/12/21 | 4/13/21 | 4/14/21 | 4/15/21 | 4/16/21 | 4/17/21 | 4/18/21 | 4/19/21 | 4/20/21 | 4/21/21 | 4/22/21 | 4/23/21 | 4/24/21 | 4/25/21 | 4/26/21 | 4/27/21 | 4/28/21 | 4/29/21 | 4/30/21 | 5/1/21 | 5/2/21 | 5/3/21 | 5/4/21 | 5/5/21 | 5/6/21 | 5/7/21 | 5/8/21 | 5/9/21 | 5/10/21 | 5/11/21 | 5/12/21 | 5/13/21 | 5/14/21 | 5/15/21 | 5/16/21 | 5/17/21 | 5/18/21 | 5/19/21 | 5/20/21 | 5/21/21 | 5/22/21 | 5/23/21 | 5/24/21 | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | 5/30/21 | 5/31/21 | 6/1/21 | 6/2/21 | 6/3/21 | 6/4/21 | 6/5/21 | 6/6/21 | 6/7/21 | 6/8/21 | 6/9/21 | 6/10/21 | 6/11/21 | 6/12/21 | 6/13/21 | 6/14/21 | 6/15/21 | 6/16/21 | 6/17/21 | 6/18/21 | 6/19/21 | 6/20/21 | 6/21/21 | 6/22/21 | 6/23/21 | 6/24/21 | 6/25/21 | 6/26/21 | 6/27/21 | 6/28/21 | 6/29/21 | 6/30/21 | 7/1/21 | 7/2/21 | 7/3/21 | 7/4/21 | 7/5/21 | 7/6/21 | 7/7/21 | 7/8/21 | 7/9/21 | 7/10/21 | 7/11/21 | 7/12/21 | 7/13/21 | 7/14/21 | 7/15/21 | 7/16/21 | 7/17/21 | 7/18/21 | 7/19/21 | 7/20/21 | 7/21/21 | 7/22/21 | 7/23/21 | 7/24/21 | 7/25/21 | 7/26/21 | 7/27/21 | 7/28/21 | 7/29/21 | 7/30/21 | 7/31/21 | 8/1/21 | 8/2/21 | 8/3/21 | 8/4/21 | 8/5/21 | 8/6/21 | 8/7/21 | 8/8/21 | 8/9/21 | 8/10/21 | 8/11/21 | 8/12/21 | 8/13/21 | 8/14/21 | 8/15/21 | 8/16/21 | 8/17/21 | 8/18/21 | 8/19/21 | 8/20/21 | 8/21/21 | 8/22/21 | 8/23/21 | 8/24/21 | 8/25/21 | 8/26/21 | 8/27/21 | 8/28/21 | 8/29/21 | 8/30/21 | 8/31/21 | 9/1/21 | 9/2/21 | 9/3/21 | 9/4/21 | 9/5/21 | 9/6/21 | 9/7/21 | 9/8/21 | 9/9/21 | 9/10/21 | 9/11/21 | 9/12/21 | 9/13/21 | 9/14/21 | 9/15/21 | 9/16/21 | 9/17/21 | 9/18/21 | 9/19/21 | 9/20/21 | 9/21/21 | 9/22/21 | 9/23/21 | 9/24/21 | 9/25/21 | 9/26/21 | 9/27/21 | 9/28/21 | 9/29/21 | 9/30/21 | 10/1/21 | 10/2/21 | 10/3/21 | 10/4/21 | 10/5/21 | 10/6/21 | 10/7/21 | 10/8/21 | 10/9/21 | 10/10/21 | 10/11/21 | 10/12/21 | 10/13/21 | 10/14/21 | 10/15/21 | 10/16/21 | 10/17/21 | 10/18/21 | 10/19/21 | 10/20/21 | 10/21/21 | 10/22/21 | 10/23/21 | 10/24/21 | 10/25/21 | 10/26/21 | 10/27/21 | 10/28/21 | 10/29/21 | 10/30/21 | 10/31/21 | 11/1/21 | 11/2/21 | 11/3/21 | 11/4/21 | 11/5/21 | 11/6/21 | 11/7/21 | 11/8/21 | 11/9/21 | 11/10/21 | 11/11/21 | 11/12/21 | 11/13/21 | 11/14/21 | 11/15/21 | 11/16/21 | 11/17/21 | 11/18/21 | 11/19/21 | 11/20/21 | 11/21/21 | 11/22/21 | 11/23/21 | 11/24/21 | 11/25/21 | 11/26/21 | 11/27/21 | 11/28/21 | 11/29/21 | 11/30/21 | 12/1/21 | 12/2/21 | 12/3/21 | 12/4/21 | 12/5/21 | 12/6/21 | 12/7/21 | 12/8/21 | 12/9/21 | 12/10/21 | 12/11/21 | 12/12/21 | 12/13/21 | 12/14/21 | 12/15/21 | 12/16/21 | 12/17/21 | 12/18/21 | 12/19/21 | 12/20/21 | 12/21/21 | 12/22/21 | 12/23/21 | 12/24/21 | 12/25/21 | 12/26/21 | 12/27/21 | 12/28/21 | 12/29/21 | 12/30/21 | 12/31/21 | 1/1/22 | 1/2/22 | 1/3/22 | 1/4/22 | 1/5/22 | 1/6/22 | 1/7/22 | 1/8/22 | 1/9/22 | 1/10/22 | 1/11/22 | 1/12/22 | 1/13/22 | 1/14/22 | 1/15/22 | 1/16/22 | 1/17/22 | 1/18/22 | 1/19/22 | 1/20/22 | 1/21/22 | 1/22/22 | 1/23/22 | 1/24/22 | 1/25/22 | 1/26/22 | 1/27/22 | 1/28/22 | 1/29/22 | 1/30/22 | 1/31/22 | 2/1/22 | 2/2/22 | 2/3/22 | 2/4/22 | 2/5/22 | 2/6/22 | 2/7/22 | 2/8/22 | 2/9/22 | 2/10/22 | 2/11/22 | 2/12/22 | 2/13/22 | 2/14/22 | 2/15/22 | 2/16/22 | 2/17/22 | 2/18/22 | 2/19/22 | 2/20/22 | 2/21/22 | 2/22/22 | 2/23/22 | 2/24/22 | 2/25/22 | 2/26/22 | 2/27/22 | 2/28/22 | 3/1/22 | 3/2/22 | 3/3/22 | 3/4/22 | 3/5/22 | 3/6/22 | 3/7/22 | 3/8/22 | 3/9/22 | 3/10/22 | 3/11/22 | 3/12/22 | 3/13/22 | 3/14/22 | 3/15/22 | 3/16/22 | 3/17/22 | 3/18/22 | 3/19/22 | 3/20/22 | 3/21/22 | 3/22/22 | 3/23/22 | 3/24/22 | 3/25/22 | 3/26/22 | 3/27/22 | 3/28/22 | 3/29/22 | 3/30/22 | 3/31/22 | 4/1/22 | 4/2/22 | 4/3/22 | 4/4/22 | 4/5/22 | 4/6/22 | 4/7/22 | 4/8/22 | 4/9/22 | 4/10/22 | 4/11/22 | 4/12/22 | 4/13/22 | 4/14/22 | 4/15/22 | 4/16/22 | 4/17/22 | 4/18/22 | 4/19/22 | 4/20/22 | 4/21/22 | 4/22/22 | 4/23/22 | 4/24/22 | 4/25/22 | 4/26/22 | 4/27/22 | 4/28/22 | 4/29/22 | 4/30/22 | 5/1/22 | 5/2/22 | 5/3/22 | 5/4/22 | 5/5/22 | 5/6/22 | 5/7/22 | 5/8/22 | 5/9/22 | 5/10/22 | 5/11/22 | 5/12/22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | nan | Afghanistan | 33.939110 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 8 | 8 | 8 | 8 | 11 | 11 | 11 | 14 | 20 | 25 | 26 | 26 | 26 | 24 | 24 | 34 | 40 | 42 | 74 | 80 | 91 | 106 | 114 | 114 | 166 | 192 | 235 | 269 | 270 | 299 | 337 | 367 | 423 | 444 | 521 | 521 | 555 | 607 | 665 | 770 | 794 | 845 | 908 | 933 | 996 | 1026 | 1092 | 1176 | 1226 | 1330 | 1463 | 1531 | 1703 | 1827 | 1827 | 2171 | 2469 | 2469 | 2469 | 2469 | 3224 | 3392 | 3563 | 3563 | 4402 | 4664 | 4967 | 4967 | 5339 | 6053 | 6402 | 6635 | 7072 | 7655 | 8145 | 8676 | 9216 | 9952 | 10668 | 11180 | 11917 | 12465 | 13102 | 13745 | 14529 | 15180 | 15836 | 16578 | 17353 | 17977 | 19055 | 19637 | 20428 | 21003 | 21308 | 22228 | 22976 | 23632 | 24188 | 24852 | 25613 | 25719 | 26960 | 27423 | 27964 | 28383 | 28919 | 29229 | 29567 | 29726 | 30261 | 30346 | 30702 | 31053 | 31324 | 31445 | 31848 | 32108 | 32410 | 32758 | 33037 | 33150 | 33470 | 33680 | 33739 | 34280 | 34437 | 34537 | 34541 | 34826 | 35026 | 35156 | 35315 | 35375 | 35561 | 35595 | 35701 | 35813 | 36001 | 36067 | 36122 | 36243 | 36349 | 36454 | 36557 | 36628 | 36628 | 36796 | 36796 | 36796 | 36833 | 36915 | 36982 | 37023 | 37101 | 37140 | 37140 | 37355 | 37431 | 37510 | 37517 | 37637 | 37682 | 37682 | 37685 | 37685 | 37845 | 37942 | 37980 | 38039 | 38085 | 38156 | 38199 | 38215 | 38226 | 38229 | 38229 | 38248 | 38282 | 38329 | 38374 | 38374 | 38390 | 38484 | 38580 | 38606 | 38630 | 38658 | 38692 | 38727 | 38802 | 38858 | 38901 | 38941 | 38958 | 38969 | 39005 | 39130 | 39160 | 39182 | 39231 | 39256 | 39272 | 39278 | 39313 | 39325 | 39340 | 39354 | 39371 | 39376 | 39383 | 39427 | 39508 | 39572 | 39634 | 39702 | 39779 | 39789 | 39885 | 39956 | 40014 | 40080 | 40112 | 40159 | 40227 | 40286 | 40373 | 40461 | 40461 | 40510 | 40626 | 40687 | 40768 | 40833 | 40937 | 41032 | 41145 | 41268 | 41334 | 41425 | 41501 | 41633 | 41728 | 41814 | 41935 | 41975 | 42033 | 42159 | 42297 | 42463 | 42609 | 42795 | 42969 | 43035 | 43240 | 43403 | 43628 | 43851 | 44228 | 44443 | 44503 | 44706 | 44988 | 45278 | 45490 | 45716 | 45839 | 45966 | 46215 | 46498 | 46717 | 46980 | 47258 | 47388 | 47641 | 47901 | 48136 | 48366 | 48540 | 48753 | 48826 | 48952 | 49273 | 49484 | 49703 | 49927 | 50202 | 50456 | 50536 | 50678 | 50888 | 51070 | 51357 | 51595 | 51764 | 51848 | 52007 | 52147 | 52330 | 52330 | 52513 | 52586 | 52709 | 52909 | 53011 | 53105 | 53207 | 53332 | 53400 | 53489 | 53538 | 53584 | 53690 | 53775 | 53831 | 53938 | 53984 | 54062 | 54141 | 54278 | 54403 | 54483 | 54559 | 54595 | 54672 | 54750 | 54854 | 54891 | 54939 | 55008 | 55023 | 55059 | 55121 | 55174 | 55231 | 55265 | 55330 | 55335 | 55359 | 55384 | 55402 | 55420 | 55445 | 55473 | 55492 | 55514 | 55518 | 55540 | 55557 | 55575 | 55580 | 55604 | 55617 | 55646 | 55664 | 55680 | 55696 | 55707 | 55714 | 55733 | 55759 | 55770 | 55775 | 55827 | 55840 | 55847 | 55876 | 55876 | 55894 | 55917 | 55959 | 55959 | 55985 | 55985 | 55995 | 56016 | 56044 | 56069 | 56093 | 56103 | 56153 | 56177 | 56192 | 56226 | 56254 | 56290 | 56294 | 56322 | 56384 | 56454 | 56517 | 56572 | 56595 | 56676 | 56717 | 56779 | 56873 | 56943 | 57019 | 57144 | 57160 | 57242 | 57364 | 57492 | 57534 | 57612 | 57721 | 57793 | 57898 | 58037 | 58214 | 58312 | 58542 | 58730 | 58843 | 59015 | 59225 | 59370 | 59576 | 59745 | 59939 | 60122 | 60300 | 60563 | 60797 | 61162 | 61455 | 61755 | 61842 | 62063 | 62403 | 62718 | 63045 | 63355 | 63412 | 63484 | 63598 | 63819 | 64122 | 64575 | 65080 | 65486 | 65728 | 66275 | 66903 | 67743 | 68366 | 69130 | 70111 | 70761 | 71838 | 72977 | 74026 | 75119 | 76628 | 77963 | 79224 | 80841 | 82326 | 84050 | 85892 | 87716 | 88740 | 89861 | 91458 | 93272 | 93288 | 96531 | 98734 | 100521 | 101906 | 103902 | 105749 | 107957 | 109532 | 111592 | 113124 | 114220 | 115751 | 117158 | 118659 | 120216 | 122156 | 123485 | 124748 | 125937 | 127464 | 129021 | 130113 | 131586 | 132777 | 133578 | 134653 | 135889 | 136643 | 137853 | 139051 | 140224 | 140602 | 141499 | 142414 | 142762 | 143183 | 143439 | 143666 | 143871 | 144285 | 145008 | 145552 | 145996 | 146523 | 147154 | 147501 | 147985 | 148572 | 148933 | 149361 | 149810 | 150240 | 150458 | 150778 | 151013 | 151291 | 151563 | 151770 | 151941 | 152033 | 152142 | 152243 | 152363 | 152411 | 152448 | 152497 | 152511 | 152583 | 152660 | 152722 | 152822 | 152960 | 153007 | 153033 | 153148 | 153220 | 153260 | 153306 | 153375 | 153395 | 153423 | 153534 | 153626 | 153736 | 153840 | 153962 | 153982 | 153990 | 154094 | 154180 | 154283 | 154361 | 154487 | 154487 | 154487 | 154585 | 154712 | 154757 | 154800 | 154960 | 154960 | 154960 | 155072 | 155093 | 155128 | 155174 | 155191 | 155191 | 155191 | 155287 | 155309 | 155380 | 155429 | 155448 | 155466 | 155508 | 155540 | 155599 | 155627 | 155682 | 155688 | 155739 | 155764 | 155776 | 155801 | 155859 | 155891 | 155931 | 155940 | 155944 | 156040 | 156071 | 156124 | 156166 | 156196 | 156210 | 156250 | 156284 | 156307 | 156323 | 156363 | 156392 | 156397 | 156397 | 156397 | 156397 | 156414 | 156456 | 156487 | 156510 | 156552 | 156610 | 156649 | 156739 | 156739 | 156812 | 156864 | 156896 | 156911 | 157015 | 157032 | 157144 | 157171 | 157190 | 157218 | 157260 | 157289 | 157359 | 157387 | 157412 | 157431 | 157454 | 157499 | 157508 | 157542 | 157585 | 157603 | 157611 | 157633 | 157648 | 157660 | 157665 | 157725 | 157734 | 157745 | 157787 | 157797 | 157816 | 157841 | 157878 | 157887 | 157895 | 157951 | 157967 | 157998 | 158037 | 158056 | 158084 | 158107 | 158189 | 158183 | 158205 | 158245 | 158275 | 158300 | 158309 | 158381 | 158394 | 158471 | 158511 | 158602 | 158639 | 158678 | 158717 | 158826 | 158974 | 159070 | 159303 | 159516 | 159548 | 159649 | 159896 | 160252 | 160692 | 161004 | 161057 | 161290 | 162111 | 162926 | 163555 | 164190 | 164727 | 165358 | 165711 | 166191 | 166924 | 167739 | 168550 | 169448 | 169940 | 170152 | 170604 | 171246 | 171422 | 171519 | 171673 | 171857 | 171931 | 172205 | 172441 | 172716 | 172901 | 173047 | 173084 | 173146 | 173395 | 173659 | 173879 | 174073 | 174214 | 174214 | 174331 | 174582 | 175000 | 175353 | 175525 | 175893 | 175974 | 176039 | 176201 | 176409 | 176571 | 176743 | 176918 | 176983 | 177039 | 177093 | 177191 | 177255 | 177321 | 177321 | 177321 | 177321 | 177520 | 177602 | 177658 | 177716 | 177747 | 177782 | 177803 | 177827 | 177897 | 177932 | 177974 | 177974 | 177974 | 177974 | 177974 | 178141 | 178257 | 178295 | 178352 | 178373 | 178387 | 178418 | 178457 | 178513 | 178574 | 178611 | 178638 | 178648 | 178689 | 178745 | 178769 | 178809 | 178850 | 178873 | 178879 | 178899 | 178901 | 178901 | 178901 | 178905 | 178919 | 178922 | 178981 | 179010 | 179017 | 179131 | 179169 |
| 1 | nan | Albania | 41.153300 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 10 | 12 | 23 | 33 | 38 | 42 | 51 | 55 | 59 | 64 | 70 | 76 | 89 | 104 | 123 | 146 | 174 | 186 | 197 | 212 | 223 | 243 | 259 | 277 | 304 | 333 | 361 | 377 | 383 | 400 | 409 | 416 | 433 | 446 | 467 | 475 | 494 | 518 | 539 | 548 | 562 | 584 | 609 | 634 | 663 | 678 | 712 | 726 | 736 | 750 | 766 | 773 | 782 | 789 | 795 | 803 | 820 | 832 | 842 | 850 | 856 | 868 | 872 | 876 | 880 | 898 | 916 | 933 | 946 | 948 | 949 | 964 | 969 | 981 | 989 | 998 | 1004 | 1029 | 1050 | 1076 | 1099 | 1122 | 1137 | 1143 | 1164 | 1184 | 1197 | 1212 | 1232 | 1246 | 1263 | 1299 | 1341 | 1385 | 1416 | 1464 | 1521 | 1590 | 1672 | 1722 | 1788 | 1838 | 1891 | 1962 | 1995 | 2047 | 2114 | 2192 | 2269 | 2330 | 2402 | 2466 | 2535 | 2580 | 2662 | 2752 | 2819 | 2893 | 2964 | 3038 | 3106 | 3188 | 3278 | 3371 | 3454 | 3571 | 3667 | 3752 | 3851 | 3906 | 4008 | 4090 | 4171 | 4290 | 4358 | 4466 | 4570 | 4637 | 4763 | 4880 | 4997 | 5105 | 5197 | 5276 | 5396 | 5519 | 5620 | 5750 | 5889 | 6016 | 6151 | 6275 | 6411 | 6536 | 6676 | 6817 | 6971 | 7117 | 7260 | 7380 | 7499 | 7654 | 7812 | 7967 | 8119 | 8275 | 8427 | 8605 | 8759 | 8927 | 9083 | 9195 | 9279 | 9380 | 9513 | 9606 | 9728 | 9844 | 9967 | 10102 | 10255 | 10406 | 10553 | 10704 | 10860 | 11021 | 11185 | 11353 | 11520 | 11672 | 11816 | 11948 | 12073 | 12226 | 12385 | 12535 | 12666 | 12787 | 12921 | 13045 | 13153 | 13259 | 13391 | 13518 | 13649 | 13806 | 13965 | 14117 | 14266 | 14410 | 14568 | 14730 | 14899 | 15066 | 15231 | 15399 | 15570 | 15752 | 15955 | 16212 | 16501 | 16774 | 17055 | 17350 | 17651 | 17948 | 18250 | 18556 | 18858 | 19157 | 19445 | 19729 | 20040 | 20315 | 20634 | 20875 | 21202 | 21523 | 21904 | 22300 | 22721 | 23210 | 23705 | 24206 | 24731 | 25294 | 25801 | 26211 | 26701 | 27233 | 27830 | 28432 | 29126 | 29837 | 30623 | 31459 | 32196 | 32761 | 33556 | 34300 | 34944 | 35600 | 36245 | 36790 | 37625 | 38182 | 39014 | 39719 | 40501 | 41302 | 42148 | 42988 | 43683 | 44436 | 45188 | 46061 | 46863 | 47742 | 48530 | 49191 | 50000 | 50637 | 51424 | 52004 | 52542 | 53003 | 53425 | 53814 | 54317 | 54827 | 55380 | 55755 | 56254 | 56572 | 57146 | 57727 | 58316 | 58316 | 58991 | 59438 | 59623 | 60283 | 61008 | 61705 | 62378 | 63033 | 63595 | 63971 | 64627 | 65334 | 65994 | 66635 | 67216 | 67690 | 67982 | 68568 | 69238 | 69916 | 70655 | 71441 | 72274 | 72812 | 73691 | 74567 | 75454 | 76350 | 77251 | 78127 | 78992 | 79934 | 80941 | 81993 | 83082 | 84212 | 85336 | 86289 | 87528 | 88671 | 89776 | 90835 | 91987 | 93075 | 93850 | 94651 | 95726 | 96838 | 97909 | 99062 | 100246 | 101285 | 102306 | 103327 | 104313 | 105229 | 106215 | 107167 | 107931 | 108823 | 109674 | 110521 | 111301 | 112078 | 112897 | 113580 | 114209 | 114840 | 115442 | 116123 | 116821 | 117474 | 118017 | 118492 | 118938 | 119528 | 120022 | 120541 | 121200 | 121544 | 121847 | 122295 | 122767 | 123216 | 123641 | 124134 | 124419 | 124723 | 125157 | 125506 | 125842 | 126183 | 126531 | 126795 | 126936 | 127192 | 127509 | 127795 | 128155 | 128393 | 128518 | 128752 | 128959 | 129128 | 129307 | 129456 | 129594 | 129694 | 129842 | 129980 | 130114 | 130270 | 130409 | 130537 | 130606 | 130736 | 130859 | 130977 | 131085 | 131185 | 131238 | 131276 | 131327 | 131419 | 131510 | 131577 | 131666 | 131723 | 131753 | 131803 | 131845 | 131890 | 131939 | 131978 | 132015 | 132032 | 132071 | 132095 | 132118 | 132153 | 132176 | 132209 | 132215 | 132229 | 132244 | 132264 | 132285 | 132297 | 132309 | 132315 | 132337 | 132351 | 132360 | 132372 | 132374 | 132379 | 132384 | 132397 | 132415 | 132426 | 132437 | 132449 | 132459 | 132461 | 132469 | 132476 | 132481 | 132484 | 132488 | 132490 | 132490 | 132496 | 132497 | 132499 | 132506 | 132509 | 132512 | 132513 | 132514 | 132521 | 132523 | 132526 | 132534 | 132535 | 132537 | 132544 | 132557 | 132565 | 132580 | 132587 | 132592 | 132597 | 132608 | 132616 | 132629 | 132647 | 132665 | 132686 | 132697 | 132740 | 132763 | 132797 | 132828 | 132853 | 132875 | 132891 | 132922 | 132952 | 132999 | 133036 | 133081 | 133121 | 133146 | 133211 | 133310 | 133442 | 133591 | 133730 | 133912 | 133981 | 134201 | 134487 | 134761 | 135140 | 135550 | 135947 | 136147 | 136598 | 137075 | 137597 | 138132 | 138790 | 139324 | 139721 | 140521 | 141365 | 142253 | 143174 | 144079 | 144847 | 145333 | 146387 | 147369 | 148222 | 149117 | 150101 | 150997 | 151499 | 152239 | 153318 | 154316 | 155293 | 156162 | 157026 | 157436 | 158431 | 159423 | 160365 | 161324 | 162173 | 162953 | 163404 | 164276 | 165096 | 165864 | 166690 | 167354 | 167893 | 168188 | 168782 | 169462 | 170131 | 170778 | 171327 | 171794 | 171794 | 172618 | 173190 | 173723 | 174168 | 174643 | 174968 | 175163 | 175664 | 176172 | 176667 | 177108 | 177536 | 177971 | 178188 | 178804 | 179463 | 180029 | 180623 | 181252 | 181696 | 181960 | 182610 | 183282 | 183873 | 184340 | 184887 | 185300 | 185497 | 186222 | 186793 | 187363 | 187994 | 187994 | 189125 | 189355 | 190125 | 190815 | 191440 | 192013 | 192600 | 193075 | 193269 | 193856 | 194472 | 195021 | 195523 | 195988 | 195988 | 196611 | 197167 | 197776 | 198292 | 198732 | 199137 | 199555 | 199750 | 199945 | 200173 | 200639 | 201045 | 201402 | 201730 | 201902 | 202295 | 202641 | 202863 | 203215 | 203524 | 203787 | 203925 | 204301 | 204627 | 204928 | 205224 | 205549 | 205777 | 205897 | 206273 | 206616 | 206935 | 207221 | 207542 | 207709 | 207709 | 208352 | 208899 | 208899 | 210224 | 210224 | 210885 | 210885 | 212021 | 212021 | 213257 | 214905 | 214905 | 219694 | 220487 | 222664 | 224569 | 226598 | 228777 | 230940 | 232637 | 233654 | 236486 | 239129 | 241512 | 244182 | 246412 | 248070 | 248070 | 248859 | 251015 | 252577 | 254126 | 254126 | 255741 | 258543 | 258543 | 261240 | 261240 | 263172 | 263172 | 264624 | 264875 | 265716 | 266416 | 267020 | 267020 | 267551 | 268008 | 268304 | 268491 | 268940 | 269301 | 269601 | 269904 | 270164 | 270370 | 270455 | 270734 | 270947 | 271141 | 271141 | 271527 | 271563 | 271702 | 271825 | 271825 | 272030 | 272030 | 272210 | 272250 | 272337 | 272412 | 272479 | 272552 | 272621 | 272663 | 272689 | 272711 | 272804 | 272885 | 272961 | 273040 | 273088 | 273088 | 273146 | 273164 | 273257 | 273318 | 273387 | 273432 | 273432 | 273529 | 273608 | 273677 | 273759 | 273823 | 273870 | 273913 | 274000 | 274055 | 274108 | 274136 | 274191 | 274219 | 274219 | 274272 | 274320 | 274376 | 274429 | 274462 | 274504 | 274520 | 274535 | 274606 | 274606 | 274737 | 274791 | 274828 | 274828 | 274862 | 274929 | 275002 | 275055 | 275107 | 275167 | 275177 | 275191 | 275211 | 275266 | 275310 | 275341 | 275366 | 275372 | 275416 | 275440 | 275485 |
| 2 | nan | Algeria | 28.033900 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 5 | 12 | 12 | 17 | 17 | 19 | 20 | 20 | 20 | 24 | 26 | 37 | 48 | 54 | 60 | 74 | 87 | 90 | 139 | 201 | 230 | 264 | 302 | 367 | 409 | 454 | 511 | 584 | 716 | 847 | 986 | 1171 | 1251 | 1320 | 1423 | 1468 | 1572 | 1666 | 1761 | 1825 | 1914 | 1983 | 2070 | 2160 | 2268 | 2418 | 2534 | 2629 | 2718 | 2811 | 2910 | 3007 | 3127 | 3256 | 3382 | 3517 | 3649 | 3848 | 4006 | 4154 | 4295 | 4474 | 4648 | 4838 | 4997 | 5182 | 5369 | 5558 | 5723 | 5891 | 6067 | 6253 | 6442 | 6629 | 6821 | 7019 | 7201 | 7377 | 7542 | 7728 | 7918 | 8113 | 8306 | 8503 | 8697 | 8857 | 8997 | 9134 | 9267 | 9394 | 9513 | 9626 | 9733 | 9831 | 9935 | 10050 | 10154 | 10265 | 10382 | 10484 | 10589 | 10698 | 10810 | 10919 | 11031 | 11147 | 11268 | 11385 | 11504 | 11631 | 11771 | 11920 | 12076 | 12248 | 12445 | 12685 | 12968 | 13273 | 13571 | 13907 | 14272 | 14657 | 15070 | 15500 | 15941 | 16404 | 16879 | 17348 | 17808 | 18242 | 18712 | 19195 | 19689 | 20216 | 20770 | 21355 | 21948 | 22549 | 23084 | 23691 | 24278 | 24872 | 25484 | 26159 | 26764 | 27357 | 27973 | 28615 | 29229 | 29831 | 30394 | 30950 | 31465 | 31972 | 32504 | 33055 | 33626 | 34155 | 34693 | 35160 | 35712 | 36204 | 36699 | 37187 | 37664 | 38133 | 38583 | 39025 | 39444 | 39847 | 40258 | 40667 | 41068 | 41460 | 41858 | 42228 | 42619 | 43016 | 43403 | 43781 | 44146 | 44494 | 44833 | 45158 | 45469 | 45773 | 46071 | 46364 | 46653 | 46938 | 47216 | 47488 | 47752 | 48007 | 48254 | 48496 | 48734 | 48966 | 49194 | 49413 | 49623 | 49826 | 50023 | 50214 | 50400 | 50579 | 50754 | 50914 | 51067 | 51213 | 51368 | 51530 | 51690 | 51847 | 51995 | 52136 | 52270 | 52399 | 52520 | 52658 | 52804 | 52940 | 53072 | 53325 | 53399 | 53584 | 53777 | 53998 | 54203 | 54402 | 54616 | 54829 | 55081 | 55357 | 55630 | 55880 | 56143 | 56419 | 56706 | 57026 | 57332 | 57651 | 57942 | 58272 | 58574 | 58979 | 59527 | 60169 | 60800 | 61381 | 62051 | 62693 | 63446 | 64257 | 65108 | 65975 | 66819 | 67679 | 68589 | 69591 | 70629 | 71652 | 72755 | 73774 | 74862 | 75867 | 77000 | 78025 | 79110 | 80168 | 81212 | 82221 | 83199 | 84152 | 85084 | 85927 | 86730 | 87502 | 88252 | 88825 | 89416 | 90014 | 90579 | 91121 | 91638 | 92102 | 92597 | 93065 | 93507 | 93933 | 94371 | 94781 | 95203 | 95659 | 96069 | 96549 | 97007 | 97441 | 97857 | 98249 | 98631 | 98988 | 99311 | 99610 | 99897 | 100159 | 100408 | 100645 | 100873 | 101120 | 101382 | 101657 | 101913 | 102144 | 102369 | 102641 | 102860 | 103127 | 103381 | 103611 | 103833 | 104092 | 104341 | 104606 | 104852 | 105124 | 105369 | 105596 | 105854 | 106097 | 106359 | 106610 | 106887 | 107122 | 107339 | 107578 | 107841 | 108116 | 108381 | 108629 | 108629 | 109088 | 109313 | 109559 | 109782 | 110049 | 110303 | 110513 | 110711 | 110894 | 111069 | 111247 | 111418 | 111600 | 111764 | 111917 | 112094 | 112279 | 112461 | 112622 | 112805 | 112960 | 113092 | 113255 | 113430 | 113593 | 113761 | 113948 | 114104 | 114234 | 114382 | 114543 | 114681 | 114851 | 115008 | 115143 | 115265 | 115410 | 115540 | 115688 | 115842 | 115970 | 116066 | 116157 | 116255 | 116349 | 116438 | 116543 | 116657 | 116750 | 116836 | 116946 | 117061 | 117192 | 117304 | 117429 | 117524 | 117622 | 117739 | 117879 | 118004 | 118116 | 118251 | 118378 | 118516 | 118645 | 118799 | 118975 | 119142 | 119323 | 119486 | 119642 | 119805 | 119992 | 120174 | 120363 | 120562 | 120736 | 120922 | 121112 | 121344 | 121580 | 121866 | 122108 | 122311 | 122522 | 122717 | 122999 | 123272 | 123473 | 123692 | 123900 | 124104 | 124288 | 124483 | 124682 | 124889 | 125059 | 125194 | 125311 | 125485 | 125693 | 125896 | 126156 | 126434 | 126651 | 126860 | 127107 | 127361 | 127646 | 127926 | 128198 | 128456 | 128725 | 128913 | 129218 | 129640 | 129976 | 130361 | 130681 | 130958 | 131283 | 131647 | 132034 | 132355 | 132727 | 133070 | 133388 | 133742 | 134115 | 134458 | 134840 | 135219 | 135586 | 135821 | 136294 | 136679 | 137049 | 137403 | 137772 | 138113 | 138465 | 138840 | 139229 | 139626 | 140075 | 140550 | 141007 | 141471 | 141966 | 142447 | 143032 | 143652 | 144483 | 145296 | 146064 | 146942 | 147883 | 148797 | 149906 | 151103 | 152210 | 153309 | 154486 | 155784 | 157005 | 158213 | 159563 | 160868 | 162155 | 163660 | 165204 | 167131 | 168668 | 170189 | 171392 | 172564 | 173922 | 175229 | 176724 | 178013 | 179216 | 180356 | 181376 | 182368 | 183347 | 184191 | 185042 | 185902 | 186655 | 187258 | 187968 | 188663 | 189384 | 190078 | 190656 | 191171 | 191583 | 192089 | 192626 | 193171 | 193674 | 194186 | 194671 | 195162 | 195574 | 196080 | 196527 | 196915 | 197308 | 197659 | 198004 | 198313 | 198645 | 198962 | 199275 | 199560 | 199822 | 200068 | 200301 | 200528 | 200770 | 200989 | 201224 | 201425 | 201600 | 201766 | 201948 | 202122 | 202283 | 202449 | 202574 | 202722 | 202877 | 203045 | 203198 | 203359 | 203517 | 203657 | 203789 | 203915 | 204046 | 204171 | 204276 | 204388 | 204490 | 204597 | 204695 | 204790 | 204900 | 205005 | 205106 | 205199 | 205286 | 205364 | 205453 | 205529 | 205599 | 205683 | 205750 | 205822 | 205903 | 205990 | 206069 | 206160 | 206270 | 206358 | 206452 | 206566 | 206649 | 206754 | 206878 | 206995 | 207079 | 207156 | 207254 | 207385 | 207509 | 207624 | 207764 | 207873 | 207970 | 208104 | 208245 | 208380 | 208532 | 208695 | 208839 | 208952 | 209111 | 209283 | 209463 | 209624 | 209817 | 209980 | 210152 | 210344 | 210531 | 210723 | 210921 | 211112 | 211297 | 211469 | 211662 | 211859 | 212047 | 212224 | 212434 | 212652 | 212848 | 213058 | 213288 | 213533 | 213745 | 214044 | 214330 | 214592 | 214835 | 215145 | 215430 | 215723 | 216098 | 216376 | 216637 | 216930 | 217265 | 217647 | 218037 | 218432 | 218818 | 219159 | 219532 | 219953 | 220415 | 220825 | 221316 | 221742 | 222157 | 222639 | 223196 | 223806 | 224383 | 224979 | 225484 | 226057 | 226749 | 227559 | 228918 | 230470 | 232325 | 234536 | 236670 | 238885 | 241406 | 243568 | 245698 | 247568 | 249310 | 250774 | 252117 | 253520 | 254885 | 255836 | 256806 | 257598 | 257976 | 258478 | 259088 | 259673 | 260191 | 260723 | 261226 | 261752 | 262165 | 262570 | 262994 | 263369 | 263685 | 263936 | 264054 | 264201 | 264365 | 264488 | 264603 | 264706 | 264778 | 264855 | 264936 | 265010 | 265079 | 265130 | 265186 | 265227 | 265265 | 265297 | 265323 | 265346 | 265366 | 265391 | 265410 | 265432 | 265457 | 265478 | 265496 | 265511 | 265524 | 265539 | 265550 | 265562 | 265573 | 265585 | 265599 | 265612 | 265621 | 265629 | 265641 | 265651 | 265662 | 265671 | 265679 | 265684 | 265691 | 265694 | 265699 | 265705 | 265707 | 265714 | 265720 | 265724 | 265727 | 265730 | 265731 | 265733 | 265738 | 265739 | 265739 | 265741 | 265746 | 265746 | 265754 | 265761 | 265761 | 265767 | 265771 | 265772 | 265773 | 265776 | 265779 | 265780 | 265782 | 265782 | 265782 | 265782 | 265786 | 265791 | 265794 | 265798 | 265800 | 265804 | 265806 | 265808 |
| 3 | nan | Andorra | 42.506300 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 39 | 39 | 53 | 75 | 88 | 113 | 133 | 164 | 188 | 224 | 267 | 308 | 334 | 370 | 376 | 390 | 428 | 439 | 466 | 501 | 525 | 545 | 564 | 583 | 601 | 601 | 638 | 646 | 659 | 673 | 673 | 696 | 704 | 713 | 717 | 717 | 723 | 723 | 731 | 738 | 738 | 743 | 743 | 743 | 745 | 745 | 747 | 748 | 750 | 751 | 751 | 752 | 752 | 754 | 755 | 755 | 758 | 760 | 761 | 761 | 761 | 761 | 761 | 761 | 762 | 762 | 762 | 762 | 762 | 763 | 763 | 763 | 763 | 764 | 764 | 764 | 765 | 844 | 851 | 852 | 852 | 852 | 852 | 852 | 852 | 852 | 852 | 853 | 853 | 853 | 853 | 854 | 854 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 855 | 858 | 861 | 862 | 877 | 880 | 880 | 880 | 884 | 884 | 889 | 889 | 897 | 897 | 897 | 907 | 907 | 918 | 922 | 925 | 925 | 925 | 937 | 939 | 939 | 944 | 955 | 955 | 955 | 963 | 963 | 977 | 981 | 989 | 989 | 989 | 1005 | 1005 | 1024 | 1024 | 1045 | 1045 | 1045 | 1060 | 1060 | 1098 | 1098 | 1124 | 1124 | 1124 | 1176 | 1184 | 1199 | 1199 | 1215 | 1215 | 1215 | 1261 | 1261 | 1301 | 1301 | 1344 | 1344 | 1344 | 1438 | 1438 | 1483 | 1483 | 1564 | 1564 | 1564 | 1681 | 1681 | 1753 | 1753 | 1836 | 1836 | 1836 | 1966 | 1966 | 2050 | 2050 | 2110 | 2110 | 2110 | 2370 | 2370 | 2568 | 2568 | 2696 | 2696 | 2696 | 2995 | 2995 | 3190 | 3190 | 3377 | 3377 | 3377 | 3623 | 3623 | 3811 | 3811 | 4038 | 4038 | 4038 | 4325 | 4410 | 4517 | 4567 | 4665 | 4756 | 4825 | 4888 | 4910 | 5045 | 5135 | 5135 | 5319 | 5383 | 5437 | 5477 | 5567 | 5616 | 5725 | 5725 | 5872 | 5914 | 5951 | 6018 | 6066 | 6142 | 6207 | 6256 | 6304 | 6351 | 6428 | 6534 | 6610 | 6610 | 6712 | 6745 | 6790 | 6842 | 6904 | 6955 | 7005 | 7050 | 7084 | 7127 | 7162 | 7190 | 7236 | 7288 | 7338 | 7382 | 7382 | 7446 | 7466 | 7519 | 7560 | 7577 | 7602 | 7633 | 7669 | 7699 | 7756 | 7806 | 7821 | 7875 | 7919 | 7983 | 8049 | 8117 | 8166 | 8192 | 8249 | 8308 | 8348 | 8348 | 8489 | 8586 | 8586 | 8586 | 8682 | 8818 | 8868 | 8946 | 9038 | 9083 | 9083 | 9194 | 9308 | 9379 | 9416 | 9499 | 9549 | 9596 | 9638 | 9716 | 9779 | 9837 | 9885 | 9937 | 9972 | 10017 | 10070 | 10137 | 10172 | 10206 | 10251 | 10275 | 10312 | 10352 | 10391 | 10427 | 10463 | 10503 | 10538 | 10555 | 10583 | 10610 | 10645 | 10672 | 10699 | 10712 | 10739 | 10775 | 10799 | 10822 | 10849 | 10866 | 10889 | 10908 | 10948 | 10976 | 10998 | 11019 | 11042 | 11069 | 11089 | 11130 | 11130 | 11199 | 11228 | 11266 | 11289 | 11319 | 11360 | 11393 | 11431 | 11481 | 11517 | 11545 | 11591 | 11638 | 11687 | 11732 | 11809 | 11850 | 11888 | 11944 | 12010 | 12053 | 12115 | 12174 | 12231 | 12286 | 12328 | 12363 | 12409 | 12456 | 12497 | 12545 | 12581 | 12614 | 12641 | 12641 | 12712 | 12771 | 12805 | 12805 | 12874 | 12917 | 12942 | 13007 | 13024 | 13060 | 13083 | 13121 | 13148 | 13198 | 13232 | 13232 | 13282 | 13295 | 13316 | 13340 | 13363 | 13390 | 13406 | 13423 | 13429 | 13447 | 13470 | 13470 | 13510 | 13510 | 13510 | 13555 | 13569 | 13569 | 13569 | 13569 | 13569 | 13569 | 13569 | 13664 | 13671 | 13682 | 13693 | 13693 | 13693 | 13727 | 13729 | 13744 | 13752 | 13758 | 13758 | 13758 | 13777 | 13781 | 13791 | 13805 | 13813 | 13813 | 13813 | 13826 | 13828 | 13836 | 13839 | 13842 | 13842 | 13842 | 13864 | 13864 | 13873 | 13877 | 13882 | 13882 | 13882 | 13882 | 13900 | 13911 | 13918 | 13918 | 13918 | 13918 | 13918 | 13991 | 14021 | 14050 | 14075 | 14075 | 14075 | 14155 | 14167 | 14167 | 14239 | 14273 | 14273 | 14273 | 14359 | 14379 | 14379 | 14464 | 14498 | 14498 | 14498 | 14577 | 14586 | 14586 | 14655 | 14678 | 14678 | 14678 | 14747 | 14766 | 14797 | 14809 | 14836 | 14836 | 14836 | 14836 | 14873 | 14891 | 14908 | 14924 | 14924 | 14924 | 14954 | 14960 | 14976 | 14981 | 14988 | 14988 | 14988 | 15002 | 15003 | 15014 | 15016 | 15025 | 15025 | 15025 | 15032 | 15033 | 15046 | 15052 | 15055 | 15055 | 15055 | 15069 | 15070 | 15070 | 15078 | 15083 | 15083 | 15083 | 15096 | 15099 | 15108 | 15113 | 15124 | 15124 | 15124 | 15140 | 15140 | 15153 | 15156 | 15167 | 15167 | 15167 | 15189 | 15192 | 15209 | 15222 | 15222 | 15222 | 15222 | 15267 | 15271 | 15284 | 15288 | 15291 | 15291 | 15291 | 15307 | 15307 | 15314 | 15326 | 15338 | 15338 | 15338 | 15367 | 15369 | 15382 | 15382 | 15404 | 15404 | 15404 | 15425 | 15425 | 15462 | 15505 | 15516 | 15516 | 15516 | 15516 | 15516 | 15572 | 15618 | 15618 | 15618 | 15618 | 15705 | 15717 | 15744 | 15744 | 15819 | 15819 | 15819 | 15907 | 15929 | 15972 | 16035 | 16086 | 16086 | 16086 | 16299 | 16342 | 16426 | 16566 | 16712 | 16712 | 16712 | 16712 | 17115 | 17426 | 17658 | 18010 | 18010 | 18010 | 18631 | 18815 | 18815 | 19272 | 19440 | 19440 | 19440 | 19440 | 20136 | 20136 | 20549 | 20549 | 20549 | 20549 | 21062 | 21062 | 21372 | 21571 | 21730 | 21730 | 21730 | 22332 | 22540 | 22823 | 23122 | 23740 | 23740 | 23740 | 24502 | 24802 | 25289 | 25289 | 26408 | 26408 | 26408 | 27983 | 28542 | 28899 | 28899 | 29888 | 29888 | 29888 | 29888 | 29888 | 29888 | 32201 | 33025 | 33025 | 33025 | 33025 | 34701 | 35028 | 35028 | 35556 | 35556 | 35556 | 35958 | 35958 | 36315 | 36470 | 36599 | 36599 | 36599 | 36808 | 36808 | 36989 | 37074 | 37140 | 37140 | 37140 | 37277 | 37361 | 37452 | 37522 | 37589 | 37589 | 37589 | 37589 | 37820 | 37901 | 37958 | 37999 | 37999 | 37999 | 37999 | 38165 | 38249 | 38342 | 38434 | 38434 | 38434 | 38620 | 38710 | 38794 | 38794 | 38794 | 38794 | 38794 | 38794 | 38794 | 38794 | 39234 | 39234 | 39234 | 39234 | 39234 | 39234 | 39713 | 39713 | 39713 | 39713 | 39713 | 39713 | 39713 | 40024 | 40024 | 40024 | 40024 | 40024 | 40024 | 40024 | 40024 | 40328 | 40328 | 40328 | 40328 | 40328 | 40328 | 40709 | 40709 | 40709 | 40709 | 40709 | 40709 | 40709 | 41013 | 41013 | 41013 | 41013 | 41013 | 41013 | 41013 | 41013 | 41349 | 41349 | 41349 | 41349 | 41349 | 41349 | 41717 | 41717 | 41717 | 41717 | 41717 | 41717 | 41717 | 41717 | 42156 |
| 4 | nan | Angola | -11.202700 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 3 | 3 | 3 | 4 | 4 | 5 | 7 | 7 | 7 | 8 | 8 | 8 | 10 | 14 | 16 | 17 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 24 | 24 | 24 | 24 | 25 | 25 | 25 | 25 | 26 | 27 | 27 | 27 | 27 | 30 | 35 | 35 | 35 | 36 | 36 | 36 | 43 | 43 | 45 | 45 | 45 | 45 | 48 | 48 | 48 | 48 | 50 | 52 | 52 | 58 | 60 | 61 | 69 | 70 | 70 | 71 | 74 | 81 | 84 | 86 | 86 | 86 | 86 | 86 | 86 | 88 | 91 | 92 | 96 | 113 | 118 | 130 | 138 | 140 | 142 | 148 | 155 | 166 | 172 | 176 | 183 | 186 | 189 | 197 | 212 | 212 | 259 | 267 | 276 | 284 | 291 | 315 | 328 | 346 | 346 | 346 | 386 | 386 | 396 | 458 | 462 | 506 | 525 | 541 | 576 | 607 | 638 | 687 | 705 | 749 | 779 | 812 | 851 | 880 | 916 | 932 | 950 | 1000 | 1078 | 1109 | 1148 | 1164 | 1199 | 1280 | 1344 | 1395 | 1483 | 1538 | 1572 | 1672 | 1679 | 1735 | 1762 | 1815 | 1852 | 1879 | 1906 | 1935 | 1966 | 2015 | 2044 | 2068 | 2134 | 2171 | 2222 | 2283 | 2332 | 2415 | 2471 | 2551 | 2624 | 2654 | 2729 | 2777 | 2805 | 2876 | 2935 | 2965 | 2981 | 3033 | 3092 | 3217 | 3279 | 3335 | 3388 | 3439 | 3569 | 3675 | 3789 | 3848 | 3901 | 3991 | 4117 | 4236 | 4363 | 4475 | 4590 | 4672 | 4718 | 4797 | 4905 | 4972 | 5114 | 5211 | 5370 | 5402 | 5530 | 5725 | 5725 | 5958 | 6031 | 6246 | 6366 | 6488 | 6680 | 6846 | 7096 | 7222 | 7462 | 7622 | 7829 | 8049 | 8338 | 8582 | 8829 | 9026 | 9381 | 9644 | 9871 | 10074 | 10269 | 10558 | 10805 | 11035 | 11228 | 11577 | 11813 | 12102 | 12223 | 12335 | 12433 | 12680 | 12816 | 12953 | 13053 | 13228 | 13374 | 13451 | 13615 | 13818 | 13922 | 14134 | 14267 | 14413 | 14493 | 14634 | 14742 | 14821 | 14920 | 15008 | 15087 | 15103 | 15139 | 15251 | 15319 | 15361 | 15493 | 15536 | 15591 | 15648 | 15729 | 15804 | 15925 | 16061 | 16161 | 16188 | 16277 | 16362 | 16407 | 16484 | 16562 | 16626 | 16644 | 16686 | 16802 | 16931 | 17029 | 17099 | 17149 | 17240 | 17296 | 17371 | 17433 | 17553 | 17568 | 17608 | 17642 | 17684 | 17756 | 17864 | 17974 | 18066 | 18156 | 18193 | 18254 | 18343 | 18425 | 18613 | 18679 | 18765 | 18875 | 18926 | 19011 | 19093 | 19177 | 19269 | 19367 | 19399 | 19476 | 19553 | 19580 | 19672 | 19723 | 19782 | 19796 | 19829 | 19900 | 19937 | 19996 | 20030 | 20062 | 20086 | 20112 | 20163 | 20210 | 20261 | 20294 | 20329 | 20366 | 20381 | 20389 | 20400 | 20452 | 20478 | 20499 | 20519 | 20548 | 20584 | 20640 | 20695 | 20759 | 20782 | 20807 | 20854 | 20882 | 20923 | 20981 | 21026 | 21055 | 21086 | 21108 | 21114 | 21161 | 21205 | 21265 | 21323 | 21380 | 21407 | 21446 | 21489 | 21558 | 21642 | 21696 | 21733 | 21757 | 21774 | 21836 | 21914 | 21961 | 22031 | 22063 | 22132 | 22182 | 22311 | 22399 | 22467 | 22579 | 22631 | 22717 | 22885 | 23010 | 23108 | 23242 | 23331 | 23457 | 23549 | 23697 | 23841 | 23951 | 24122 | 24300 | 24389 | 24518 | 24661 | 24883 | 25051 | 25279 | 25492 | 25609 | 25710 | 25942 | 26168 | 26431 | 26652 | 26815 | 26993 | 27133 | 27284 | 27529 | 27921 | 28201 | 28477 | 28740 | 28875 | 29146 | 29405 | 29695 | 30030 | 30354 | 30637 | 30787 | 31045 | 31438 | 31661 | 31909 | 32149 | 32441 | 32623 | 32933 | 33338 | 33607 | 33944 | 34180 | 34366 | 34551 | 34752 | 34960 | 35140 | 35307 | 35594 | 35772 | 35854 | 36004 | 36115 | 36325 | 36455 | 36600 | 36705 | 36790 | 36921 | 37094 | 37289 | 37467 | 37604 | 37678 | 37748 | 37874 | 38002 | 38091 | 38371 | 38528 | 38556 | 38613 | 38682 | 38849 | 38965 | 39089 | 39172 | 39230 | 39300 | 39375 | 39491 | 39593 | 39791 | 39881 | 39958 | 40055 | 40138 | 40327 | 40530 | 40631 | 40707 | 40805 | 40906 | 41061 | 41227 | 41405 | 41629 | 41736 | 41780 | 41879 | 42110 | 42288 | 42486 | 42646 | 42777 | 42815 | 42970 | 43070 | 43158 | 43269 | 43487 | 43592 | 43662 | 43747 | 43890 | 43998 | 44174 | 44328 | 44534 | 44617 | 44739 | 44972 | 45175 | 45325 | 45583 | 45817 | 45945 | 46076 | 46340 | 46539 | 46726 | 46929 | 47079 | 47168 | 47331 | 47544 | 47781 | 48004 | 48261 | 48475 | 48656 | 48790 | 49114 | 49349 | 49628 | 49943 | 50348 | 50446 | 50738 | 51047 | 51407 | 51827 | 52208 | 52307 | 52307 | 52644 | 52968 | 53387 | 53840 | 54280 | 54795 | 55121 | 55583 | 56040 | 56583 | 56583 | 58076 | 58603 | 58943 | 58943 | 59895 | 60448 | 60803 | 61023 | 61245 | 61378 | 61580 | 61794 | 62143 | 62385 | 62606 | 62789 | 62842 | 63012 | 63197 | 63340 | 63567 | 63691 | 63775 | 63861 | 63930 | 64033 | 64126 | 64226 | 64301 | 64374 | 64433 | 64458 | 64487 | 64533 | 64583 | 64612 | 64654 | 64674 | 64724 | 64762 | 64815 | 64857 | 64875 | 64899 | 64913 | 64913 | 64940 | 64968 | 64985 | 64997 | 65011 | 65024 | 65033 | 65061 | 65080 | 65105 | 65130 | 65139 | 65144 | 65155 | 65168 | 65183 | 65208 | 65223 | 65244 | 65259 | 65259 | 65301 | 65332 | 65346 | 65371 | 65397 | 65404 | 65404 | 65431 | 65565 | 65648 | 65760 | 65868 | 65938 | 66086 | 66566 | 67199 | 68362 | 70221 | 71142 | 71752 | 71752 | 76787 | 78475 | 79871 | 81593 | 82398 | 82920 | 83764 | 84666 | 86636 | 87625 | 88775 | 89251 | 89718 | 90316 | 91148 | 91907 | 92581 | 93302 | 93524 | 93694 | 93974 | 94275 | 94779 | 95220 | 95676 | 95902 | 96582 | 97263 | 97594 | 97812 | 97901 | 98029 | 98057 | 98076 | 98116 | 98226 | 98267 | 98319 | 98340 | 98351 | 98364 | 98409 | 98424 | 98453 | 98474 | 98501 | 98514 | 98514 | 98514 | 98555 | 98568 | 98585 | 98605 | 98617 | 98638 | 98658 | 98671 | 98698 | 98701 | 98701 | 98701 | 98701 | 98741 | 98746 | 98746 | 98746 | 98796 | 98796 | 98806 | 98806 | 98829 | 98855 | 98855 | 98855 | 98909 | 98927 | 98931 | 98956 | 98985 | 99003 | 99003 | 99003 | 99003 | 99010 | 99058 | 99058 | 99081 | 99102 | 99106 | 99115 | 99115 | 99138 | 99138 | 99169 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99194 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 | 99287 |
| 5 | nan | Antarctica | -71.949900 | 23.347000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 |
| 6 | nan | Antigua and Barbuda | 17.060800 | -61.796400 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 3 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 9 | 15 | 15 | 15 | 15 | 19 | 19 | 19 | 19 | 21 | 21 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 23 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 65 | 65 | 65 | 69 | 69 | 69 | 69 | 69 | 68 | 68 | 68 | 70 | 70 | 70 | 73 | 74 | 74 | 74 | 74 | 74 | 74 | 74 | 76 | 76 | 76 | 76 | 76 | 76 | 76 | 82 | 82 | 82 | 86 | 86 | 91 | 91 | 91 | 91 | 91 | 92 | 92 | 92 | 92 | 92 | 92 | 92 | 92 | 92 | 92 | 92 | 93 | 93 | 93 | 93 | 93 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 96 | 96 | 96 | 96 | 97 | 97 | 98 | 98 | 101 | 101 | 101 | 101 | 101 | 106 | 107 | 107 | 107 | 107 | 108 | 111 | 111 | 111 | 111 | 111 | 111 | 112 | 112 | 112 | 119 | 119 | 119 | 119 | 122 | 122 | 122 | 124 | 124 | 124 | 124 | 124 | 124 | 127 | 128 | 128 | 128 | 128 | 130 | 130 | 130 | 131 | 131 | 131 | 131 | 131 | 131 | 133 | 134 | 134 | 134 | 134 | 139 | 139 | 139 | 139 | 139 | 139 | 139 | 140 | 141 | 141 | 141 | 141 | 141 | 142 | 144 | 144 | 144 | 144 | 144 | 146 | 146 | 146 | 146 | 147 | 148 | 148 | 148 | 148 | 151 | 151 | 152 | 152 | 153 | 153 | 153 | 154 | 154 | 155 | 155 | 155 | 158 | 158 | 158 | 159 | 159 | 159 | 160 | 160 | 160 | 163 | 163 | 167 | 169 | 176 | 176 | 176 | 176 | 184 | 184 | 187 | 189 | 189 | 190 | 190 | 192 | 195 | 195 | 198 | 201 | 201 | 215 | 215 | 218 | 218 | 234 | 234 | 249 | 249 | 268 | 277 | 288 | 299 | 316 | 316 | 350 | 381 | 419 | 427 | 427 | 443 | 443 | 525 | 548 | 548 | 598 | 598 | 614 | 636 | 646 | 701 | 701 | 726 | 730 | 769 | 769 | 769 | 813 | 813 | 813 | 848 | 848 | 862 | 882 | 882 | 945 | 962 | 963 | 963 | 992 | 992 | 1008 | 1011 | 1033 | 1033 | 1072 | 1080 | 1080 | 1103 | 1122 | 1122 | 1128 | 1136 | 1136 | 1136 | 1147 | 1152 | 1170 | 1170 | 1173 | 1173 | 1177 | 1180 | 1182 | 1197 | 1198 | 1198 | 1201 | 1201 | 1209 | 1213 | 1216 | 1216 | 1217 | 1217 | 1217 | 1217 | 1222 | 1227 | 1227 | 1228 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1232 | 1231 | 1237 | 1238 | 1240 | 1240 | 1240 | 1241 | 1241 | 1251 | 1251 | 1252 | 1255 | 1255 | 1257 | 1257 | 1258 | 1258 | 1258 | 1258 | 1259 | 1259 | 1259 | 1260 | 1260 | 1262 | 1262 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1263 | 1264 | 1264 | 1264 | 1264 | 1264 | 1265 | 1265 | 1266 | 1266 | 1266 | 1266 | 1266 | 1266 | 1267 | 1267 | 1268 | 1268 | 1268 | 1275 | 1275 | 1275 | 1277 | 1277 | 1280 | 1280 | 1280 | 1288 | 1288 | 1295 | 1303 | 1303 | 1303 | 1303 | 1303 | 1311 | 1320 | 1328 | 1328 | 1338 | 1348 | 1348 | 1372 | 1372 | 1372 | 1378 | 1397 | 1397 | 1397 | 1421 | 1421 | 1447 | 1490 | 1490 | 1540 | 1540 | 1598 | 1598 | 1598 | 1638 | 1651 | 1713 | 1715 | 1719 | 1742 | 1750 | 1759 | 1870 | 1878 | 1960 | 1974 | 2059 | 2059 | 2166 | 2166 | 2297 | 2304 | 2304 | 2304 | 2603 | 2603 | 2603 | 2603 | 2603 | 2625 | 2815 | 2815 | 2902 | 2923 | 2923 | 3160 | 3188 | 3231 | 3336 | 3403 | 3503 | 3503 | 3518 | 3581 | 3663 | 3678 | 3738 | 3750 | 3750 | 3772 | 3817 | 3830 | 3858 | 3888 | 3888 | 3918 | 3918 | 3939 | 3984 | 3994 | 4019 | 4019 | 4031 | 4031 | 4036 | 4040 | 4040 | 4058 | 4058 | 4062 | 4069 | 4069 | 4072 | 4078 | 4091 | 4091 | 4091 | 4091 | 4102 | 4102 | 4106 | 4118 | 4118 | 4118 | 4122 | 4129 | 4129 | 4131 | 4135 | 4135 | 4136 | 4138 | 4138 | 4141 | 4141 | 4141 | 4141 | 4141 | 4141 | 4141 | 4141 | 4146 | 4147 | 4147 | 4148 | 4148 | 4151 | 4151 | 4159 | 4159 | 4162 | 4162 | 4177 | 4177 | 4178 | 4186 | 4198 | 4198 | 4198 | 4201 | 4205 | 4216 | 4216 | 4236 | 4236 | 4259 | 4259 | 4259 | 4283 | 4283 | 4283 | 4283 | 4283 | 4486 | 4486 | 4715 | 4715 | 4844 | 5058 | 5058 | 5058 | 5214 | 5214 | 5246 | 5321 | 5321 | 5321 | 5346 | 5741 | 5741 | 5815 | 5931 | 5931 | 6023 | 6023 | 6442 | 6524 | 6558 | 6558 | 6558 | 6627 | 6627 | 6732 | 6732 | 6732 | 6732 | 6853 | 6853 | 6853 | 6853 | 7321 | 7331 | 7331 | 7331 | 7331 | 7342 | 7395 | 7395 | 7400 | 7408 | 7408 | 7408 | 7408 | 7429 | 7435 | 7437 | 7437 | 7437 | 7437 | 7447 | 7449 | 7449 | 7449 | 7455 | 7455 | 7455 | 7455 | 7461 | 7461 | 7466 | 7466 | 7466 | 7466 | 7470 | 7470 | 7470 | 7470 | 7473 | 7473 | 7473 | 7473 | 7482 | 7482 | 7482 | 7482 | 7485 | 7491 | 7491 | 7491 | 7491 | 7493 | 7493 | 7493 | 7493 | 7493 | 7493 | 7511 | 7511 | 7511 | 7511 | 7511 | 7523 | 7523 | 7535 | 7535 | 7535 | 7539 | 7539 | 7539 | 7567 | 7567 | 7571 | 7571 | 7571 | 7571 | 7571 | 7571 | 7604 | 7626 | 7626 | 7626 | 7626 | 7626 | 7654 | 7663 | 7663 | 7663 | 7663 | 7663 | 7663 | 7721 | 7721 |
| 7 | nan | Argentina | -38.416100 | -63.616700 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 8 | 12 | 12 | 17 | 19 | 19 | 31 | 34 | 45 | 56 | 68 | 79 | 97 | 128 | 158 | 266 | 301 | 387 | 387 | 502 | 589 | 690 | 745 | 820 | 1054 | 1054 | 1133 | 1265 | 1451 | 1451 | 1554 | 1628 | 1715 | 1795 | 1975 | 1975 | 2142 | 2208 | 2277 | 2443 | 2571 | 2669 | 2758 | 2839 | 2941 | 3031 | 3144 | 3435 | 3607 | 3780 | 3892 | 4003 | 4127 | 4285 | 4428 | 4532 | 4681 | 4783 | 4887 | 5020 | 5208 | 5371 | 5611 | 5776 | 6034 | 6278 | 6563 | 6879 | 7134 | 7479 | 7805 | 8068 | 8371 | 8809 | 9283 | 9931 | 10649 | 11353 | 12076 | 12628 | 13228 | 13933 | 14702 | 15419 | 16214 | 16851 | 17415 | 18319 | 19268 | 20197 | 21037 | 22020 | 22794 | 23620 | 24761 | 25987 | 27373 | 28764 | 30295 | 31577 | 32785 | 34159 | 35552 | 37510 | 39570 | 41204 | 42785 | 44931 | 47203 | 49851 | 52457 | 55343 | 57744 | 59933 | 62268 | 64530 | 67197 | 69941 | 72786 | 75376 | 77815 | 80447 | 83426 | 87030 | 90693 | 94060 | 97509 | 100166 | 103265 | 106910 | 111146 | 114783 | 119301 | 122524 | 126755 | 130774 | 136118 | 141900 | 148027 | 153520 | 158334 | 162526 | 167416 | 173355 | 178996 | 185373 | 191302 | 196543 | 201919 | 206743 | 213535 | 220682 | 228195 | 235677 | 241811 | 246499 | 253868 | 260911 | 268574 | 276072 | 282437 | 289100 | 294569 | 299126 | 305966 | 312659 | 320884 | 329043 | 336802 | 342154 | 350867 | 359638 | 370188 | 380292 | 392009 | 401239 | 408426 | 417735 | 428239 | 439172 | 451198 | 461882 | 471806 | 478792 | 488007 | 500034 | 512293 | 524198 | 535705 | 546481 | 555537 | 565446 | 577338 | 589012 | 601713 | 613658 | 622934 | 631365 | 640147 | 652174 | 664799 | 678266 | 691235 | 702484 | 711325 | 723132 | 736609 | 751001 | 765002 | 779689 | 790818 | 798486 | 809728 | 824468 | 840915 | 856369 | 871468 | 883882 | 894206 | 903730 | 917035 | 931967 | 949063 | 965609 | 979119 | 989680 | 1002662 | 1018999 | 1037325 | 1053650 | 1069368 | 1081336 | 1090589 | 1102301 | 1116609 | 1130533 | 1143800 | 1157179 | 1166924 | 1173533 | 1183131 | 1195276 | 1205928 | 1217028 | 1228814 | 1236851 | 1242182 | 1250499 | 1262476 | 1273356 | 1284519 | 1296378 | 1304846 | 1310491 | 1318384 | 1329005 | 1339337 | 1349434 | 1359042 | 1366182 | 1370366 | 1374631 | 1381795 | 1390388 | 1399431 | 1407277 | 1413375 | 1418807 | 1424533 | 1432570 | 1440103 | 1447732 | 1454631 | 1459832 | 1463110 | 1466309 | 1469919 | 1475222 | 1482216 | 1489328 | 1494602 | 1498160 | 1503222 | 1510203 | 1517046 | 1524372 | 1531374 | 1537169 | 1541285 | 1547138 | 1555279 | 1563865 | 1563865 | 1574554 | 1578267 | 1583297 | 1590513 | 1602163 | 1613928 | 1625514 | 1629594 | 1634834 | 1640718 | 1648940 | 1662730 | 1676171 | 1690006 | 1703352 | 1714409 | 1722217 | 1730921 | 1744704 | 1757429 | 1770715 | 1783047 | 1791979 | 1799243 | 1807428 | 1819569 | 1831681 | 1843077 | 1853830 | 1862192 | 1867223 | 1874801 | 1885210 | 1896053 | 1905524 | 1915362 | 1922264 | 1927239 | 1933853 | 1943548 | 1952744 | 1961635 | 1970009 | 1976689 | 1980347 | 1985501 | 1993295 | 2001034 | 2008345 | 2015496 | 2021553 | 2025798 | 2029057 | 2033060 | 2039124 | 2046795 | 2054681 | 2060625 | 2064334 | 2069751 | 2077228 | 2085411 | 2093645 | 2098728 | 2104197 | 2107365 | 2112023 | 2118676 | 2126531 | 2133963 | 2141854 | 2146714 | 2149636 | 2154694 | 2162001 | 2169694 | 2177898 | 2185747 | 2192025 | 2195722 | 2201886 | 2210121 | 2218425 | 2226753 | 2234913 | 2241739 | 2245771 | 2252172 | 2261577 | 2269877 | 2278115 | 2291051 | 2301389 | 2308597 | 2322611 | 2332765 | 2348821 | 2363251 | 2373153 | 2383537 | 2393492 | 2407159 | 2428029 | 2450068 | 2473751 | 2497881 | 2517300 | 2532562 | 2551999 | 2579000 | 2604157 | 2629156 | 2658628 | 2677747 | 2694014 | 2714475 | 2743620 | 2769552 | 2796768 | 2824652 | 2845872 | 2860884 | 2879677 | 2905172 | 2928890 | 2954943 | 2977363 | 2993865 | 3005259 | 3021179 | 3047417 | 3071496 | 3095582 | 3118134 | 3136158 | 3147740 | 3165121 | 3191097 | 3215572 | 3242103 | 3269466 | 3290935 | 3307285 | 3335965 | 3371508 | 3411160 | 3447044 | 3482512 | 3514683 | 3539484 | 3562135 | 3586736 | 3622135 | 3663215 | 3702422 | 3732263 | 3753609 | 3781784 | 3817139 | 3852156 | 3884447 | 3915397 | 3939024 | 3955439 | 3977634 | 4008771 | 4038528 | 4066156 | 4093090 | 4111147 | 4124190 | 4145482 | 4172742 | 4198620 | 4222400 | 4242763 | 4258394 | 4268789 | 4277395 | 4298782 | 4326101 | 4350564 | 4374587 | 4393142 | 4405247 | 4423636 | 4447701 | 4470374 | 4491551 | 4512439 | 4526473 | 4535473 | 4552750 | 4574340 | 4593763 | 4613019 | 4627537 | 4639098 | 4647948 | 4662937 | 4682960 | 4702657 | 4719952 | 4737213 | 4749443 | 4756378 | 4769142 | 4784219 | 4798851 | 4812351 | 4827973 | 4839109 | 4846615 | 4859170 | 4875927 | 4891810 | 4905925 | 4919408 | 4929764 | 4935847 | 4947030 | 4961880 | 4975616 | 4989402 | 5002951 | 5012754 | 5018895 | 5029075 | 5041487 | 5052884 | 5066253 | 5074725 | 5080908 | 5084635 | 5088271 | 5096443 | 5106207 | 5116803 | 5124963 | 5130852 | 5133831 | 5139966 | 5148085 | 5155079 | 5161926 | 5167733 | 5171458 | 5173531 | 5178889 | 5185620 | 5190948 | 5195601 | 5199919 | 5202405 | 5203802 | 5207695 | 5211801 | 5215332 | 5218993 | 5221809 | 5223604 | 5224534 | 5226831 | 5229848 | 5232358 | 5234851 | 5237159 | 5238610 | 5239232 | 5241394 | 5243231 | 5245265 | 5246998 | 5248847 | 5249840 | 5250402 | 5251940 | 5253765 | 5255261 | 5256902 | 5258466 | 5259352 | 5259738 | 5260719 | 5261935 | 5263219 | 5264305 | 5265058 | 5265528 | 5265859 | 5266275 | 5267339 | 5268653 | 5270003 | 5271361 | 5272151 | 5272551 | 5273463 | 5274766 | 5275984 | 5277525 | 5278910 | 5279818 | 5280358 | 5281585 | 5283000 | 5284485 | 5286074 | 5287447 | 5288259 | 5288807 | 5289945 | 5291285 | 5292549 | 5293989 | 5295260 | 5296188 | 5296781 | 5298069 | 5299418 | 5300985 | 5302445 | 5304059 | 5305151 | 5305742 | 5307159 | 5308781 | 5310334 | 5312089 | 5313607 | 5314702 | 5315348 | 5315989 | 5317633 | 5319867 | 5322127 | 5324039 | 5325560 | 5326448 | 5328416 | 5330748 | 5332629 | 5335310 | 5337692 | 5339382 | 5340676 | 5343153 | 5346242 | 5348123 | 5350867 | 5354440 | 5356885 | 5358455 | 5361967 | 5366522 | 5371341 | 5376642 | 5382290 | 5386453 | 5389707 | 5395044 | 5404380 | 5415501 | 5428957 | 5445236 | 5452419 | 5460042 | 5480305 | 5514207 | 5556239 | 5606745 | 5654408 | 5674428 | 5694930 | 5739326 | 5820536 | 5915695 | 6025303 | 6135836 | 6237525 | 6310844 | 6399196 | 6533635 | 6664717 | 6793119 | 6932972 | 7029624 | 7094865 | 7197323 | 7318305 | 7446626 | 7576335 | 7694506 | 7792652 | 7862536 | 7940657 | 8041520 | 8130023 | 8207752 | 8271636 | 8313614 | 8335184 | 8378656 | 8427778 | 8472848 | 8515285 | 8555379 | 8577215 | 8589879 | 8615285 | 8648075 | 8675327 | 8700437 | 8716940 | 8728262 | 8734551 | 8747601 | 8766174 | 8783208 | 8799858 | 8815247 | 8823054 | 8827504 | 8838674 | 8855624 | 8868188 | 8878486 | 8887973 | 8893568 | 8897178 | 8900656 | 8904176 | 8912317 | 8921536 | 8929898 | 8934328 | 8936602 | 8942888 | 8949362 | 8955458 | 8961595 | 8967210 | 8970196 | 8971432 | 8976079 | 8981155 | 8985836 | 8990413 | 9004829 | 9006526 | 9007753 | 9011367 | 9016057 | 9019660 | 9021240 | 9023812 | 9025257 | 9026075 | 9028730 | 9032162 | 9035127 | 9037911 | 9039838 | 9040640 | 9041124 | 9043098 | 9045326 | 9047408 | 9049250 | 9051243 | 9052083 | 9052536 | 9054126 | 9056203 | 9057923 | 9059351 | 9059944 | 9060495 | 9060923 | 9060923 | 9060923 | 9060923 | 9060923 | 9060923 | 9060923 | 9060923 | 9072230 | 9072230 | 9072230 | 9072230 | 9072230 | 9072230 | 9083673 | 9083673 | 9083673 | 9083673 | 9083673 | 9083673 | 9083673 | 9101319 | 9101319 | 9101319 | 9101319 | 9101319 |
| 8 | nan | Armenia | 40.069100 | 45.038200 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 8 | 18 | 26 | 52 | 78 | 84 | 115 | 136 | 160 | 194 | 235 | 249 | 265 | 290 | 329 | 407 | 424 | 482 | 532 | 571 | 663 | 736 | 770 | 822 | 833 | 853 | 881 | 921 | 937 | 967 | 1013 | 1039 | 1067 | 1111 | 1159 | 1201 | 1248 | 1291 | 1339 | 1401 | 1473 | 1523 | 1596 | 1677 | 1746 | 1808 | 1867 | 1932 | 2066 | 2148 | 2273 | 2386 | 2507 | 2619 | 2782 | 2884 | 3029 | 3175 | 3313 | 3392 | 3538 | 3718 | 3860 | 4044 | 4283 | 4472 | 4823 | 5041 | 5271 | 5606 | 5928 | 6302 | 6661 | 7113 | 7402 | 7774 | 8216 | 8676 | 8927 | 9282 | 9492 | 10009 | 10524 | 11221 | 11817 | 12364 | 13130 | 13325 | 13675 | 14103 | 14669 | 15281 | 16004 | 16667 | 17064 | 17489 | 18033 | 18698 | 19157 | 19708 | 20268 | 20588 | 21006 | 21717 | 22488 | 23247 | 23909 | 24645 | 25127 | 25542 | 26065 | 26658 | 27320 | 27900 | 28606 | 28936 | 29285 | 29820 | 30346 | 30903 | 31392 | 31969 | 32151 | 32490 | 33005 | 33559 | 34001 | 34462 | 34877 | 34981 | 35254 | 35693 | 36162 | 36613 | 36996 | 37317 | 37390 | 37629 | 37937 | 38196 | 38550 | 38841 | 39050 | 39102 | 39298 | 39586 | 39819 | 39985 | 40185 | 40410 | 40433 | 40593 | 40794 | 41023 | 41299 | 41495 | 41663 | 41701 | 41846 | 42056 | 42319 | 42477 | 42616 | 42792 | 42825 | 42936 | 43067 | 43270 | 43451 | 43626 | 43750 | 43781 | 43878 | 44075 | 44271 | 44461 | 44649 | 44783 | 44845 | 44953 | 45152 | 45326 | 45503 | 45675 | 45862 | 45969 | 46119 | 46376 | 46671 | 46910 | 47154 | 47431 | 47552 | 47667 | 47877 | 48251 | 48643 | 49072 | 49400 | 49574 | 49901 | 50359 | 50850 | 51382 | 51925 | 52496 | 52677 | 53083 | 53755 | 54473 | 55087 | 55736 | 56451 | 56821 | 57566 | 58624 | 59995 | 61460 | 63000 | 64694 | 65460 | 66694 | 68530 | 70836 | 73310 | 75523 | 77837 | 78810 | 80410 | 82651 | 85034 | 87432 | 89813 | 92254 | 93448 | 94776 | 97150 | 99563 | 101773 | 104249 | 106424 | 107466 | 108687 | 110548 | 112680 | 114383 | 115855 | 117337 | 117886 | 118870 | 120459 | 121979 | 123646 | 124839 | 126224 | 126709 | 127522 | 129085 | 130870 | 132346 | 133594 | 134768 | 135124 | 135967 | 137231 | 138508 | 139692 | 140959 | 141937 | 142344 | 142928 | 144066 | 145240 | 146317 | 147312 | 148325 | 148682 | 149120 | 150218 | 151392 | 152253 | 153173 | 153825 | 154065 | 154602 | 155440 | 156142 | 156763 | 157349 | 157834 | 157948 | 158296 | 158878 | 159409 | 159738 | 159798 | 160027 | 160220 | 160544 | 160853 | 161054 | 161415 | 161794 | 162131 | 162288 | 162643 | 163128 | 163576 | 163972 | 164235 | 164586 | 164676 | 164912 | 165221 | 165528 | 165711 | 165909 | 166036 | 166094 | 166232 | 166427 | 166669 | 166728 | 166901 | 167026 | 167088 | 167231 | 167421 | 167568 | 167726 | 167937 | 168088 | 168177 | 168300 | 168496 | 168676 | 168830 | 169022 | 169167 | 169255 | 169391 | 169597 | 169820 | 170011 | 170234 | 170402 | 170506 | 170672 | 170945 | 171227 | 171510 | 171793 | 172058 | 172216 | 172456 | 172816 | 173307 | 173749 | 174257 | 174679 | 175016 | 175198 | 175538 | 176286 | 177104 | 177899 | 178385 | 178702 | 179287 | 180141 | 181165 | 182056 | 183127 | 183713 | 184219 | 185020 | 186184 | 187441 | 188446 | 189540 | 190317 | 190741 | 191491 | 192639 | 193736 | 194852 | 196044 | 196634 | 197113 | 197873 | 198898 | 200129 | 201158 | 202167 | 202817 | 203327 | 204053 | 205128 | 206142 | 207103 | 207973 | 208520 | 208818 | 209485 | 210518 | 211399 | 212114 | 212878 | 213288 | 213469 | 214064 | 214872 | 215528 | 216064 | 216596 | 216863 | 217008 | 217407 | 217900 | 218325 | 218681 | 219092 | 219270 | 219353 | 219596 | 219950 | 220217 | 220447 | 220729 | 220860 | 220927 | 221139 | 221368 | 221559 | 221699 | 221880 | 221948 | 221982 | 222139 | 222269 | 222409 | 222513 | 222555 | 222636 | 222670 | 222778 | 222870 | 222978 | 223050 | 223143 | 223180 | 223212 | 223285 | 223384 | 223460 | 223555 | 223643 | 223682 | 223723 | 223805 | 223904 | 224000 | 224086 | 224167 | 224227 | 224253 | 224330 | 224430 | 224533 | 224635 | 224728 | 224797 | 224851 | 224967 | 225095 | 225221 | 225339 | 225464 | 225553 | 225606 | 225661 | 225801 | 225987 | 226135 | 226285 | 226388 | 226459 | 226597 | 226756 | 226949 | 227111 | 227298 | 227430 | 227522 | 227716 | 227936 | 228161 | 228382 | 228632 | 228798 | 228910 | 229090 | 229370 | 229603 | 229867 | 230110 | 230339 | 230476 | 230713 | 230993 | 231322 | 231625 | 231923 | 232157 | 232297 | 232610 | 233001 | 233400 | 233797 | 234227 | 234558 | 234814 | 235171 | 235675 | 236234 | 236742 | 237249 | 237634 | 237885 | 238422 | 239056 | 239739 | 240261 | 240953 | 241336 | 241611 | 242135 | 242750 | 243386 | 243981 | 244602 | 245025 | 245264 | 245765 | 246410 | 246997 | 247666 | 248397 | 248850 | 249146 | 249803 | 250559 | 251323 | 252082 | 253093 | 253600 | 253942 | 254436 | 254709 | 255648 | 256554 | 257620 | 258545 | 259007 | 259779 | 260675 | 261697 | 262631 | 263783 | 264690 | 265317 | 266208 | 267363 | 268672 | 269874 | 271205 | 272356 | 272957 | 273860 | 275077 | 276666 | 278431 | 280294 | 281991 | 283183 | 284237 | 286303 | 288906 | 291052 | 293014 | 295368 | 296552 | 298069 | 300143 | 302450 | 304546 | 306739 | 308326 | 309397 | 310629 | 312674 | 315004 | 316839 | 319016 | 320433 | 321243 | 322364 | 324039 | 325521 | 326830 | 328081 | 328963 | 329341 | 329913 | 330895 | 331914 | 332713 | 333583 | 334075 | 334347 | 334878 | 335738 | 336330 | 337005 | 337522 | 337931 | 338120 | 338518 | 339020 | 339578 | 339977 | 340396 | 340723 | 340818 | 341058 | 341468 | 341768 | 342115 | 342405 | 342538 | 342604 | 342765 | 342977 | 343157 | 343350 | 343506 | 343636 | 343708 | 343845 | 343997 | 344126 | 344261 | 344379 | 344481 | 344540 | 344649 | 344737 | 344826 | 344930 | 344980 | 345007 | 345036 | 345126 | 345255 | 345389 | 345518 | 345713 | 345855 | 345981 | 346224 | 346811 | 346811 | 347084 | 347377 | 347617 | 347785 | 348145 | 348708 | 349329 | 349957 | 350897 | 351711 | 352399 | 353731 | 355662 | 358218 | 361754 | 364348 | 366433 | 367795 | 370922 | 374878 | 379266 | 383458 | 387490 | 389957 | 391588 | 394074 | 396885 | 399727 | 402403 | 404805 | 406379 | 407074 | 408381 | 410155 | 411878 | 413295 | 414764 | 415464 | 415757 | 416510 | 417456 | 418220 | 418792 | 419423 | 419693 | 419832 | 420156 | 420498 | 420757 | 421008 | 421226 | 421341 | 421401 | 421541 | 421592 | 421714 | 421842 | 421953 | 422004 | 422021 | 422076 | 422155 | 422202 | 422254 | 422286 | 422307 | 422328 | 422354 | 422382 | 422401 | 422423 | 422444 | 422458 | 422468 | 422484 | 422498 | 422519 | 422540 | 422563 | 422574 | 422581 | 422594 | 422610 | 422629 | 422643 | 422662 | 422677 | 422678 | 422691 | 422711 | 422721 | 422729 | 422747 | 422762 | 422770 | 422784 | 422799 | 422805 | 422814 | 422822 | 422825 | 422828 | 422838 | 422855 | 422858 | 422865 | 422867 | 422871 | 422874 | 422874 | 422877 | 422877 | 422877 | 422877 | 422877 | 422896 | 422896 | 422900 | 422900 |
| 9 | Australian Capital Territory | Australia | -35.473500 | 149.012400 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 2 | 3 | 4 | 6 | 9 | 19 | 32 | 39 | 39 | 53 | 62 | 71 | 77 | 78 | 80 | 84 | 87 | 91 | 93 | 96 | 96 | 96 | 99 | 100 | 103 | 103 | 103 | 102 | 103 | 103 | 103 | 103 | 103 | 103 | 104 | 104 | 104 | 104 | 105 | 106 | 106 | 106 | 106 | 106 | 106 | 106 | 106 | 106 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 107 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 108 | 111 | 112 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 114 | 115 | 115 | 115 | 115 | 115 | 115 | 115 | 115 | 115 | 116 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 117 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 118 | 120 | 120 | 120 | 120 | 122 | 122 | 122 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 129 | 131 | 133 | 152 | 169 | 191 | 207 | 219 | 226 | 245 | 261 | 291 | 300 | 314 | 335 | 361 | 374 | 386 | 398 | 421 | 433 | 451 | 483 | 498 | 509 | 528 | 548 | 563 | 587 | 602 | 617 | 630 | 652 | 665 | 680 | 710 | 725 | 742 | 749 | 765 | 782 | 798 | 817 | 849 | 874 | 893 | 906 | 928 | 959 | 1011 | 1063 | 1101 | 1129 | 1162 | 1190 | 1231 | 1271 | 1296 | 1326 | 1358 | 1386 | 1437 | 1483 | 1518 | 1538 | 1571 | 1588 | 1612 | 1636 | 1664 | 1677 | 1701 | 1710 | 1719 | 1731 | 1741 | 1749 | 1759 | 1768 | 1775 | 1780 | 1788 | 1803 | 1816 | 1822 | 1840 | 1853 | 1866 | 1884 | 1893 | 1902 | 1917 | 1928 | 1943 | 1953 | 1965 | 1971 | 1996 | 2013 | 2027 | 2042 | 2053 | 2072 | 2087 | 2095 | 2103 | 2110 | 2117 | 2124 | 2130 | 2134 | 2142 | 2146 | 2153 | 2159 | 2165 | 2167 | 2175 | 2179 | 2179 | 2196 | 2197 | 2200 | 2204 | 2210 | 2222 | 2242 | 2260 | 2278 | 2291 | 2307 | 2365 | 2450 | 2552 | 2694 | 2765 | 2942 | 3186 | 3311 | 3559 | 3564 | 4010 | 4919 | 5323 | 5323 | 7054 | 8021 | 9553 | 9429 | 12300 | 13248 | 13248 | 15834 | 17378 | 19908 | 21174 | 22396 | 23761 | 23761 | 27618 | 28510 | 28472 | 29240 | 30634 | 31366 | 31941 | 33071 | 33933 | 34418 | 34976 | 35520 | 36031 | 36474 | 37023 | 37426 | 37875 | 38143 | 38432 | 38698 | 38698 | 39613 | 40113 | 40538 | 40946 | 41380 | 41755 | 42163 | 42720 | 43246 | 43768 | 44100 | 44651 | 45086 | 45662 | 46591 | 47247 | 48187 | 48670 | 49088 | 49541 | 50214 | 51244 | 51916 | 52944 | 53607 | 54149 | 54683 | 55321 | 56474 | 57278 | 58021 | 58687 | 59305 | 59881 | 60654 | 61857 | 63148 | 64219 | 65341 | 66267 | 67243 | 68257 | 69571 | 70659 | 71683 | 72571 | 73344 | 74017 | 75421 | 76537 | 77714 | 78696 | 79515 | 80229 | 80964 | 81821 | 81821 | 82970 | 85177 | 86102 | 86887 | 87634 | 88543 | 89510 | 90567 | 90567 | 92330 | 92330 | 93713 | 94486 | 95625 | 96854 | 97825 | 98760 | 99485 | 99485 | 100927 | 101899 | 101899 | 103220 | 103220 | 104941 | 105739 | 106705 | 107769 | 108830 | 109879 | 110846 | 111599 | 112407 | 113368 | 114564 | 115688 | 116905 |
pd.set_option('display.max_rows', deaths.shape[0])
pd.set_option('display.max_columns', None)
deaths.style.set_properties(subset=['ad_description'], **{'width-max': '100px'})
deaths.tail(10).style.background_gradient(cmap='cool')
print(deaths.shape[0])
284
deaths['Country/Region'].value_counts().head(20)
China 34 Canada 16 United Kingdom 14 France 12 Australia 8 Netherlands 5 Denmark 3 New Zealand 2 Andorra 1 Zambia 1 Sweden 1 Slovenia 1 San Marino 1 Sudan 1 North Macedonia 1 Benin 1 Cameroon 1 El Salvador 1 Burma 1 Pakistan 1 Name: Country/Region, dtype: int64
pd.set_option('display.max_rows', recovered.shape[0])
pd.set_option('display.max_columns', None)
recovered.style.set_properties(subset=['ad_description'], **{'width-max': '100px'})
recovered.tail(10).style.background_gradient(cmap='cool')
print(recovered.shape[0])
269
recovered.describe().style.background_gradient(cmap='terrain')
| Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | 2/1/20 | 2/2/20 | 2/3/20 | 2/4/20 | 2/5/20 | 2/6/20 | 2/7/20 | 2/8/20 | 2/9/20 | 2/10/20 | 2/11/20 | 2/12/20 | 2/13/20 | 2/14/20 | 2/15/20 | 2/16/20 | 2/17/20 | 2/18/20 | 2/19/20 | 2/20/20 | 2/21/20 | 2/22/20 | 2/23/20 | 2/24/20 | 2/25/20 | 2/26/20 | 2/27/20 | 2/28/20 | 2/29/20 | 3/1/20 | 3/2/20 | 3/3/20 | 3/4/20 | 3/5/20 | 3/6/20 | 3/7/20 | 3/8/20 | 3/9/20 | 3/10/20 | 3/11/20 | 3/12/20 | 3/13/20 | 3/14/20 | 3/15/20 | 3/16/20 | 3/17/20 | 3/18/20 | 3/19/20 | 3/20/20 | 3/21/20 | 3/22/20 | 3/23/20 | 3/24/20 | 3/25/20 | 3/26/20 | 3/27/20 | 3/28/20 | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | 4/8/20 | 4/9/20 | 4/10/20 | 4/11/20 | 4/12/20 | 4/13/20 | 4/14/20 | 4/15/20 | 4/16/20 | 4/17/20 | 4/18/20 | 4/19/20 | 4/20/20 | 4/21/20 | 4/22/20 | 4/23/20 | 4/24/20 | 4/25/20 | 4/26/20 | 4/27/20 | 4/28/20 | 4/29/20 | 4/30/20 | 5/1/20 | 5/2/20 | 5/3/20 | 5/4/20 | 5/5/20 | 5/6/20 | 5/7/20 | 5/8/20 | 5/9/20 | 5/10/20 | 5/11/20 | 5/12/20 | 5/13/20 | 5/14/20 | 5/15/20 | 5/16/20 | 5/17/20 | 5/18/20 | 5/19/20 | 5/20/20 | 5/21/20 | 5/22/20 | 5/23/20 | 5/24/20 | 5/25/20 | 5/26/20 | 5/27/20 | 5/28/20 | 5/29/20 | 5/30/20 | 5/31/20 | 6/1/20 | 6/2/20 | 6/3/20 | 6/4/20 | 6/5/20 | 6/6/20 | 6/7/20 | 6/8/20 | 6/9/20 | 6/10/20 | 6/11/20 | 6/12/20 | 6/13/20 | 6/14/20 | 6/15/20 | 6/16/20 | 6/17/20 | 6/18/20 | 6/19/20 | 6/20/20 | 6/21/20 | 6/22/20 | 6/23/20 | 6/24/20 | 6/25/20 | 6/26/20 | 6/27/20 | 6/28/20 | 6/29/20 | 6/30/20 | 7/1/20 | 7/2/20 | 7/3/20 | 7/4/20 | 7/5/20 | 7/6/20 | 7/7/20 | 7/8/20 | 7/9/20 | 7/10/20 | 7/11/20 | 7/12/20 | 7/13/20 | 7/14/20 | 7/15/20 | 7/16/20 | 7/17/20 | 7/18/20 | 7/19/20 | 7/20/20 | 7/21/20 | 7/22/20 | 7/23/20 | 7/24/20 | 7/25/20 | 7/26/20 | 7/27/20 | 7/28/20 | 7/29/20 | 7/30/20 | 7/31/20 | 8/1/20 | 8/2/20 | 8/3/20 | 8/4/20 | 8/5/20 | 8/6/20 | 8/7/20 | 8/8/20 | 8/9/20 | 8/10/20 | 8/11/20 | 8/12/20 | 8/13/20 | 8/14/20 | 8/15/20 | 8/16/20 | 8/17/20 | 8/18/20 | 8/19/20 | 8/20/20 | 8/21/20 | 8/22/20 | 8/23/20 | 8/24/20 | 8/25/20 | 8/26/20 | 8/27/20 | 8/28/20 | 8/29/20 | 8/30/20 | 8/31/20 | 9/1/20 | 9/2/20 | 9/3/20 | 9/4/20 | 9/5/20 | 9/6/20 | 9/7/20 | 9/8/20 | 9/9/20 | 9/10/20 | 9/11/20 | 9/12/20 | 9/13/20 | 9/14/20 | 9/15/20 | 9/16/20 | 9/17/20 | 9/18/20 | 9/19/20 | 9/20/20 | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | 10/1/20 | 10/2/20 | 10/3/20 | 10/4/20 | 10/5/20 | 10/6/20 | 10/7/20 | 10/8/20 | 10/9/20 | 10/10/20 | 10/11/20 | 10/12/20 | 10/13/20 | 10/14/20 | 10/15/20 | 10/16/20 | 10/17/20 | 10/18/20 | 10/19/20 | 10/20/20 | 10/21/20 | 10/22/20 | 10/23/20 | 10/24/20 | 10/25/20 | 10/26/20 | 10/27/20 | 10/28/20 | 10/29/20 | 10/30/20 | 10/31/20 | 11/1/20 | 11/2/20 | 11/3/20 | 11/4/20 | 11/5/20 | 11/6/20 | 11/7/20 | 11/8/20 | 11/9/20 | 11/10/20 | 11/11/20 | 11/12/20 | 11/13/20 | 11/14/20 | 11/15/20 | 11/16/20 | 11/17/20 | 11/18/20 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269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 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269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 |
| mean | 18.700014 | 27.186914 | 0.111524 | 0.118959 | 0.144981 | 0.156134 | 0.208178 | 0.241636 | 0.401487 | 0.472119 | 0.539033 | 0.836431 | 1.066914 | 1.769517 | 2.330855 | 3.185874 | 4.200743 | 5.550186 | 7.494424 | 9.728625 | 12.070632 | 14.680297 | 17.412639 | 19.148699 | 23.397770 | 29.966543 | 34.933086 | 40.397770 | 46.791822 | 53.353160 | 59.936803 | 67.587361 | 70.237918 | 85.100372 | 86.977695 | 93.788104 | 103.743494 | 112.966543 | 123.724907 | 136.483271 | 147.899628 | 158.814126 | 169.535316 | 179.282528 | 190.223048 | 199.985130 | 207.672862 | 216.944238 | 225.631970 | 232.323420 | 239.412639 | 249.066914 | 254.007435 | 261.171004 | 269.970260 | 282.643123 | 290.282528 | 300.483271 | 309.728625 | 315.828996 | 324.918216 | 340.706320 | 363.817844 | 365.631970 | 401.390335 | 422.873606 | 453.996283 | 486.654275 | 518.293680 | 553.446097 | 610.457249 | 660.977695 | 717.750929 | 781.479554 | 838.810409 | 914.691450 | 966.126394 | 1028.241636 | 1114.717472 | 1222.085502 | 1315.966543 | 1396.776952 | 1494.360595 | 1566.416357 | 1667.743494 | 1760.955390 | 1896.788104 | 2011.594796 | 2108.933086 | 2197.654275 | 2315.806691 | 2398.252788 | 2527.628253 | 2639.468401 | 2746.271375 | 2932.252788 | 3035.431227 | 3142.539033 | 3246.197026 | 3369.661710 | 3526.081784 | 3768.148699 | 3909.996283 | 4061.115242 | 4182.055762 | 4308.769517 | 4444.356877 | 4615.802974 | 4762.736059 | 4898.100372 | 5096.765799 | 5222.449814 | 5396.743494 | 5534.446097 | 5742.148699 | 5889.773234 | 6068.594796 | 6278.301115 | 6430.747212 | 6627.460967 | 6820.962825 | 7040.182156 | 7230.591078 | 7634.111524 | 7837.955390 | 8044.375465 | 8281.286245 | 8486.312268 | 8721.728625 | 8970.289963 | 9257.520446 | 9519.416357 | 9803.371747 | 10007.553903 | 10394.587361 | 10688.605948 | 10949.089219 | 11206.208178 | 11474.624535 | 11679.490706 | 12243.100372 | 12548.936803 | 12843.252788 | 13162.505576 | 13458.862454 | 13778.163569 | 14041.431227 | 14339.851301 | 14703.654275 | 15145.405204 | 15447.126394 | 15800.249071 | 16230.933086 | 16486.271375 | 16826.914498 | 17213.736059 | 17644.037175 | 17989.330855 | 18383.308550 | 18778.423792 | 19110.033457 | 19461.598513 | 19897.806691 | 20328.713755 | 21387.947955 | 21796.453532 | 22524.185874 | 22967.914498 | 23427.762082 | 23966.918216 | 24554.486989 | 25054.925651 | 25573.312268 | 26040.568773 | 26456.315985 | 26977.962825 | 27506.780669 | 28100.855019 | 28667.713755 | 29349.576208 | 29910.122677 | 30237.475836 | 30829.029740 | 31478.457249 | 32146.605948 | 32801.736059 | 33650.434944 | 34467.579926 | 35000.297398 | 35637.408922 | 36271.081784 | 37024.189591 | 37817.940520 | 38559.970260 | 39232.501859 | 39748.204461 | 40575.137546 | 41406.576208 | 42230.721190 | 42932.605948 | 43646.531599 | 44390.933086 | 45055.535316 | 45665.985130 | 46800.066914 | 47694.657993 | 48304.011152 | 49356.955390 | 49983.736059 | 50849.736059 | 51634.167286 | 52480.245353 | 53290.643123 | 54063.096654 | 54689.468401 | 55469.828996 | 56274.776952 | 57016.118959 | 57876.895911 | 58716.055762 | 59486.453532 | 60226.669145 | 61012.591078 | 61794.152416 | 62540.583643 | 63499.550186 | 64314.557621 | 65137.490706 | 65931.174721 | 66678.342007 | 67465.669145 | 68200.048327 | 68950.223048 | 69843.408922 | 70648.840149 | 71480.327138 | 72309.078067 | 73007.397770 | 73856.836431 | 74698.052045 | 75551.126394 | 76369.754647 | 77303.394052 | 78184.379182 | 79065.706320 | 79978.263941 | 80793.851301 | 81773.081784 | 82723.527881 | 83612.776952 | 84587.286245 | 85382.375465 | 86209.669145 | 87106.635688 | 88029.379182 | 88852.955390 | 89493.420074 | 90443.609665 | 91269.479554 | 92152.260223 | 93017.040892 | 93965.706320 | 94781.713755 | 95550.449814 | 96276.862454 | 97104.520446 | 97848.457249 | 98563.866171 | 99353.029740 | 100123.762082 | 100878.460967 | 101615.572491 | 102364.684015 | 103117.587361 | 103903.635688 | 104688.847584 | 105531.944238 | 106300.472119 | 107087.018587 | 107820.319703 | 108709.297398 | 110901.509294 | 111890.215613 | 112807.505576 | 113758.338290 | 114666.624535 | 115625.535316 | 116666.936803 | 117602.152416 | 118640.513011 | 119769.342007 | 120841.315985 | 121952.899628 | 122894.312268 | 123849.899628 | 124806.628253 | 126220.100372 | 127061.137546 | 128184.304833 | 129217.089219 | 130074.933086 | 131527.460967 | 133139.323420 | 134489.174721 | 135880.620818 | 137201.899628 | 138463.126394 | 139480.687732 | 141009.360595 | 142392.925651 | 144018.985130 | 145382.836431 | 146801.520446 | 148138.717472 | 149321.925651 | 150817.301115 | 152587.576208 | 154308.490706 | 155895.791822 | 157719.646840 | 159340.271375 | 160515.156134 | 162050.988848 | 163689.542751 | 165274.970260 | 167101.089219 | 168878.561338 | 174473.144981 | 175941.988848 | 153674.342007 | 155005.806691 | 156352.349442 | 157747.921933 | 158929.066914 | 160236.078067 | 161265.182156 | 162541.092937 | 163891.223048 | 165096.204461 | 166265.988848 | 167350.026022 | 168867.364312 | 169954.308550 | 171063.698885 | 172376.319703 | 173819.609665 | 174928.650558 | 176056.174721 | 177004.817844 | 178002.892193 | 179082.475836 | 180254.821561 | 181418.855019 | 182623.059480 | 183728.126394 | 184955.802974 | 185961.565056 | 187040.702602 | 188317.866171 | 189678.687732 | 191064.728625 | 192256.631970 | 193435.275093 | 194608.936803 | 195793.892193 | 197291.825279 | 198510.304833 | 199871.416357 | 201290.587361 | 202467.141264 | 203623.115242 | 204792.040892 | 206017.107807 | 207283.907063 | 208457.360595 | 209810.918216 | 211116.368030 | 212190.342007 | 213203.881041 | 214505.702602 | 215731.899628 | 216824.795539 | 217870.472119 | 218965.479554 | 219996.836431 | 220862.390335 | 222243.832714 | 223244.977695 | 224271.315985 | 225340.457249 | 226255.858736 | 226982.368030 | 228055.278810 | 228930.390335 | 230023.107807 | 230902.728625 | 231774.981413 | 232775.784387 | 233583.431227 | 234262.130112 | 235297.929368 | 236076.587361 | 236923.026022 | 237890.565056 | 238822.617100 | 239473.832714 | 240272.814126 | 241276.918216 | 242129.736059 | 243060.144981 | 243901.631970 | 244780.825279 | 245666.773234 | 246687.434944 | 247662.278810 | 248537.208178 | 249550.654275 | 250549.003717 | 251487.784387 | 252401.122677 | 253478.739777 | 254469.390335 | 255452.795539 | 256453.078067 | 257490.479554 | 258450.371747 | 259491.631970 | 260735.605948 | 261899.702602 | 263147.442379 | 264306.613383 | 265661.951673 | 266796.721190 | 268018.981413 | 269150.713755 | 270396.970260 | 271818.936803 | 273100.669145 | 274291.029740 | 275651.628253 | 276946.007435 | 278238.992565 | 279614.442379 | 281411.115242 | 282880.479554 | 284567.144981 | 285998.349442 | 287970.364312 | 289437.769517 | 291246.178439 | 292907.479554 | 294559.531599 | 296433.572491 | 298361.215613 | 300518.059480 | 302508.301115 | 304756.765799 | 306661.226766 | 309026.639405 | 311405.197026 | 313547.542751 | 315531.981413 | 318087.245353 | 320618.486989 | 322836.572491 | 325466.000000 | 328018.401487 | 330555.390335 | 333131.650558 | 335523.802974 | 337965.810409 | 340618.159851 | 343129.728625 | 345717.568773 | 348360.710037 | 350824.624535 | 353161.747212 | 355953.583643 | 358360.828996 | 360926.319703 | 363399.728625 | 365788.342007 | 368269.397770 | 371036.914498 | 373799.416357 | 376394.312268 | 378761.185874 | 381046.643123 | 383339.434944 | 385205.498141 | 387538.553903 | 389683.769517 | 391749.988848 | 394342.397770 | 396405.457249 | 398292.446097 | 400113.743494 | 402080.914498 | 404251.483271 | 406215.698885 | 407811.200743 | 409764.739777 | 411542.174721 | 413183.535316 | 414752.297398 | 416294.066914 | 417976.947955 | 419514.825279 | 420910.855019 | 422170.981413 | 423768.234201 | 425057.356877 | 426767.933086 | 428066.174721 | 429480.591078 | 430668.557621 | 431902.200743 | 432887.680297 | 434215.628253 | 435343.524164 | 436829.557621 | 437906.550186 | 439128.286245 | 440223.301115 | 441185.156134 | 442402.464684 | 443493.230483 | 444537.620818 | 446128.163569 | 447210.104089 | 448212.985130 | 449324.092937 | 450688.379182 | 451907.126394 | 452904.014870 | 453991.936803 | 455252.598513 | 456903.914498 | 457983.788104 | 459107.342007 | 460161.048327 | 461386.851301 | 462431.669145 | 463884.104089 | 464927.929368 | 466104.446097 | 467244.672862 | 468434.933086 | 469574.646840 | 470898.542751 | 471814.490706 | 473737.263941 | 474954.427509 | 476071.379182 | 477357.026022 | 478608.561338 | 479903.684015 | 481045.657993 | 482175.505576 | 483250.516729 | 484303.096654 | 485434.947955 | 486613.609665 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| std | 25.330841 | 73.614830 | 1.711086 | 1.714940 | 1.903792 | 1.967499 | 2.591851 | 2.786758 | 4.911394 | 5.402128 | 5.559327 | 8.703405 | 10.414685 | 18.179424 | 23.809846 | 32.239236 | 39.299803 | 50.786522 | 69.258472 | 89.550061 | 111.592905 | 137.925549 | 163.697268 | 167.863396 | 214.997956 | 294.841449 | 346.839401 | 408.912977 | 483.666972 | 560.793213 | 634.778972 | 723.488443 | 730.179498 | 937.803134 | 941.097707 | 1026.751127 | 1162.020133 | 1283.613697 | 1430.567070 | 1614.330326 | 1772.060671 | 1926.823584 | 2072.834807 | 2211.341141 | 2354.427234 | 2478.530743 | 2566.500518 | 2656.943638 | 2763.559128 | 2840.545824 | 2917.853921 | 3003.313525 | 3075.254398 | 3150.776823 | 3237.170856 | 3325.006620 | 3378.154494 | 3435.451342 | 3495.180992 | 3544.307353 | 3593.417602 | 3642.636594 | 3685.142618 | 3711.816333 | 3767.048218 | 3820.622827 | 3883.489065 | 3954.445576 | 4037.264923 | 4123.861205 | 4260.994622 | 4384.936385 | 4534.558717 | 4735.297950 | 4920.337534 | 5152.561846 | 5352.756268 | 5542.263846 | 5893.429126 | 6403.939753 | 6812.167497 | 7112.933373 | 7526.394357 | 7809.059851 | 8245.781522 | 8601.107358 | 9031.255172 | 9439.912114 | 9788.088435 | 10102.747096 | 10501.902834 | 10838.416121 | 11197.197957 | 11610.002915 | 12017.591661 | 12917.744253 | 13169.779375 | 13593.028647 | 13971.731541 | 14379.591738 | 14932.678563 | 16346.986985 | 16937.226671 | 17656.384245 | 18045.709427 | 18553.530321 | 18936.904229 | 19408.325802 | 19899.575145 | 20287.468670 | 21078.741079 | 21450.713332 | 22285.438837 | 22485.513038 | 23304.316416 | 23688.032147 | 24187.259664 | 25147.309780 | 25541.989045 | 26296.902899 | 26837.228119 | 27442.091633 | 27976.819696 | 30441.572776 | 31257.675086 | 31806.058554 | 32602.515115 | 33248.293781 | 33986.056912 | 34853.239591 | 35710.426125 | 36656.113587 | 38186.865908 | 39041.097203 | 40070.346843 | 41350.683941 | 42258.790830 | 43138.457056 | 44104.435913 | 44780.212223 | 47970.461125 | 49186.999607 | 50385.403074 | 51552.378587 | 52668.023861 | 53828.913073 | 54681.454362 | 55857.286574 | 56976.126398 | 58922.030888 | 60068.634510 | 61390.842199 | 63182.025804 | 64069.547121 | 65585.100706 | 67295.631863 | 69315.137343 | 70711.171980 | 72297.942885 | 74055.580130 | 75367.288114 | 76912.387393 | 79091.913116 | 81039.674199 | 88702.673900 | 90574.538894 | 94842.628733 | 97205.891207 | 99574.089002 | 102346.695709 | 104842.810168 | 107250.852059 | 110164.986919 | 112270.790815 | 114007.552768 | 116444.547525 | 118988.060541 | 121689.941699 | 124802.397315 | 127355.693344 | 129345.714916 | 130600.313170 | 134477.037920 | 137725.074245 | 141076.585476 | 143949.785278 | 148886.613448 | 154341.344785 | 156920.250176 | 160144.813385 | 163066.482248 | 167361.192667 | 170643.135307 | 174750.320104 | 178181.653522 | 180602.022725 | 184752.822706 | 189143.586147 | 193394.295391 | 197180.629082 | 201145.405653 | 205409.529934 | 208932.533736 | 212199.271930 | 217769.469103 | 223048.785124 | 226020.964826 | 232585.541209 | 235233.038774 | 238890.810665 | 243511.053943 | 248565.711172 | 253378.438627 | 257816.933846 | 260917.784645 | 265829.713362 | 269610.232414 | 273704.229298 | 278829.320257 | 283952.502938 | 288210.891781 | 292452.225988 | 297137.225442 | 301142.888088 | 305557.634447 | 311445.391231 | 316408.084620 | 321435.813735 | 325919.772831 | 330503.592648 | 334452.878593 | 338804.481193 | 343229.976827 | 348369.299813 | 353326.434433 | 358346.357599 | 362976.864219 | 366473.189830 | 371973.676288 | 377351.030976 | 382688.084499 | 387693.927713 | 392805.215292 | 398652.963290 | 403856.080352 | 409775.693135 | 415072.901473 | 420584.388188 | 426929.248177 | 432305.018619 | 437801.865411 | 442142.677100 | 447380.893319 | 453015.240424 | 458706.710682 | 463557.783884 | 467205.881140 | 473494.999848 | 477778.582647 | 482574.344899 | 487790.302783 | 493514.696317 | 498579.767374 | 503251.125265 | 507863.214106 | 512260.339747 | 516588.974578 | 520491.090632 | 525010.545969 | 528880.211699 | 532768.466511 | 536783.719485 | 540325.970664 | 544515.515559 | 548125.665231 | 552584.197520 | 556888.166485 | 560720.999337 | 564527.920346 | 567834.365341 | 571919.731621 | 586962.317651 | 591440.341444 | 595572.058459 | 599851.189820 | 604084.458323 | 607710.849117 | 612134.839011 | 616144.389477 | 620307.947655 | 623932.691267 | 628541.317888 | 632597.471313 | 636039.558137 | 639748.939568 | 643498.410955 | 648521.150522 | 653004.008056 | 657139.281042 | 660749.904157 | 663663.874885 | 669114.818135 | 673301.020659 | 677998.437948 | 682554.363718 | 687094.046994 | 691389.794114 | 694186.090471 | 699741.094736 | 704275.251169 | 710505.932867 | 714331.314919 | 719335.723014 | 723679.270609 | 727267.191096 | 732122.606516 | 739841.456484 | 746080.689455 | 751680.924937 | 756832.239797 | 763190.849326 | 766769.732955 | 772284.315958 | 778359.532821 | 783550.098689 | 790610.996108 | 797843.574324 | 807433.398619 | 812465.448313 | 724089.473093 | 728167.864906 | 731863.129007 | 736312.897633 | 739420.545372 | 743840.621279 | 746486.012926 | 750784.278925 | 754150.937499 | 757002.266357 | 759316.701851 | 761533.715138 | 767996.466838 | 770609.652482 | 773829.776216 | 777444.332220 | 781697.615493 | 784836.562874 | 788197.186607 | 790125.973258 | 792250.283640 | 795887.380959 | 799230.548327 | 802341.918188 | 805762.576128 | 808469.798945 | 813324.209276 | 815828.829390 | 818944.525521 | 821356.420330 | 825330.546865 | 828806.364884 | 832000.697554 | 835393.785534 | 837928.341774 | 841351.856574 | 845362.134772 | 847473.875144 | 851243.119077 | 855645.125866 | 858413.856627 | 861459.481181 | 864618.299636 | 867428.247070 | 871343.996544 | 874013.062813 | 878638.727081 | 883102.297299 | 885487.398033 | 887961.423613 | 891612.530265 | 895311.959851 | 898814.736953 | 901495.336254 | 904121.437528 | 906833.168731 | 908581.131333 | 913636.188174 | 916406.449137 | 918766.801855 | 922074.624663 | 925010.739518 | 926959.911514 | 930142.159307 | 932334.705511 | 936626.196525 | 938863.597457 | 941115.714211 | 945204.128890 | 947178.116877 | 948780.588353 | 952938.236100 | 954971.977330 | 957881.371071 | 960853.047795 | 964247.854251 | 965661.970437 | 968703.668210 | 972471.743422 | 975285.690510 | 978666.389078 | 981467.149949 | 984210.642012 | 987382.020746 | 991870.992988 | 995312.576161 | 998331.139110 | 1001974.151392 | 1005674.244416 | 1008743.459058 | 1011246.934274 | 1015834.399539 | 1019734.031689 | 1023609.844924 | 1027402.994490 | 1031168.541960 | 1034420.950195 | 1037374.724985 | 1043202.673334 | 1047221.096734 | 1052354.524983 | 1056772.992029 | 1061898.879082 | 1066267.499238 | 1070583.423566 | 1073723.281005 | 1078682.686896 | 1084912.176570 | 1090125.792155 | 1094371.012501 | 1099737.935035 | 1103496.098534 | 1108735.382040 | 1114274.573074 | 1122954.938660 | 1129419.451065 | 1135825.748318 | 1141330.095474 | 1150743.661810 | 1156361.669884 | 1164179.578084 | 1171483.107351 | 1179347.441235 | 1188638.614867 | 1197532.146623 | 1207935.037815 | 1217224.001865 | 1228470.867047 | 1237497.263371 | 1250303.648875 | 1262929.629729 | 1274075.140141 | 1284714.909091 | 1299906.297252 | 1315860.930945 | 1329564.819249 | 1346659.357985 | 1362722.057764 | 1378683.351645 | 1395166.386801 | 1411828.587155 | 1428908.857021 | 1447431.704184 | 1464293.734234 | 1483224.759202 | 1502372.301794 | 1520462.023416 | 1538239.672173 | 1559055.309377 | 1577142.771241 | 1595712.574307 | 1614472.182435 | 1632665.715319 | 1652833.284272 | 1676301.509017 | 1698629.782424 | 1719542.396176 | 1738537.766104 | 1757211.223835 | 1775990.679427 | 1791346.785790 | 1809638.429163 | 1826083.031902 | 1841870.686634 | 1860680.719623 | 1876509.070937 | 1891013.750270 | 1904656.783701 | 1919279.094894 | 1934564.837188 | 1947786.051462 | 1958793.506771 | 1972242.002063 | 1984422.397807 | 1995277.264686 | 2005588.789215 | 2015373.933917 | 2026242.737357 | 2035250.673404 | 2043329.158357 | 2050845.852946 | 2060891.366185 | 2068001.257672 | 2077014.120937 | 2084120.529926 | 2091671.868717 | 2097933.042462 | 2104486.651085 | 2108956.880752 | 2115646.615246 | 2120822.658371 | 2128841.334642 | 2133543.010461 | 2139083.061290 | 2143961.132038 | 2147830.865856 | 2153450.569944 | 2158128.377322 | 2162427.415484 | 2170884.353520 | 2175330.479459 | 2179017.275542 | 2183365.839291 | 2189177.687777 | 2194031.783461 | 2197528.148190 | 2201351.161233 | 2205079.708211 | 2212326.266568 | 2215505.922033 | 2219026.046476 | 2222338.461802 | 2227410.922570 | 2230452.094878 | 2236448.061111 | 2239291.314410 | 2243250.163176 | 2246623.298089 | 2250576.327816 | 2254087.273846 | 2258547.885439 | 2259398.141847 | 2268996.803367 | 2272711.701615 | 2275586.348533 | 2278753.884834 | 2282152.023229 | 2285779.188599 | 2288370.724792 | 2291043.496582 | 2293539.352797 | 2296131.243041 | 2298652.198069 | 2301423.092410 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 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| min | -71.949900 | -178.116500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 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| 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 4.454096 | -9.496274 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 | 3.000000 | 4.000000 | 4.000000 | 5.000000 | 6.000000 | 6.000000 | 7.000000 | 7.000000 | 8.000000 | 9.000000 | 10.000000 | 11.000000 | 11.000000 | 11.000000 | 12.000000 | 13.000000 | 13.000000 | 15.000000 | 16.000000 | 16.000000 | 16.000000 | 17.000000 | 18.000000 | 18.000000 | 18.000000 | 19.000000 | 21.000000 | 24.000000 | 24.000000 | 24.000000 | 24.000000 | 26.000000 | 26.000000 | 26.000000 | 27.000000 | 30.000000 | 32.000000 | 32.000000 | 37.000000 | 37.000000 | 39.000000 | 39.000000 | 40.000000 | 42.000000 | 42.000000 | 42.000000 | 43.000000 | 44.000000 | 45.000000 | 49.000000 | 52.000000 | 53.000000 | 55.000000 | 60.000000 | 60.000000 | 61.000000 | 61.000000 | 61.000000 | 61.000000 | 68.000000 | 68.000000 | 69.000000 | 73.000000 | 75.000000 | 78.000000 | 78.000000 | 81.000000 | 83.000000 | 93.000000 | 93.000000 | 95.000000 | 98.000000 | 98.000000 | 98.000000 | 102.000000 | 102.000000 | 102.000000 | 105.000000 | 105.000000 | 105.000000 | 107.000000 | 108.000000 | 108.000000 | 108.000000 | 117.000000 | 117.000000 | 117.000000 | 117.000000 | 118.000000 | 118.000000 | 118.000000 | 118.000000 | 124.000000 | 124.000000 | 133.000000 | 133.000000 | 136.000000 | 124.000000 | 124.000000 | 127.000000 | 128.000000 | 128.000000 | 128.000000 | 128.000000 | 128.000000 | 128.000000 | 138.000000 | 141.000000 | 141.000000 | 138.000000 | 138.000000 | 138.000000 | 138.000000 | 138.000000 | 144.000000 | 145.000000 | 145.000000 | 145.000000 | 145.000000 | 145.000000 | 145.000000 | 145.000000 | 145.000000 | 160.000000 | 165.000000 | 165.000000 | 165.000000 | 165.000000 | 167.000000 | 167.000000 | 167.000000 | 167.000000 | 183.000000 | 183.000000 | 192.000000 | 192.000000 | 199.000000 | 199.000000 | 201.000000 | 201.000000 | 213.000000 | 217.000000 | 217.000000 | 218.000000 | 217.000000 | 217.000000 | 217.000000 | 217.000000 | 217.000000 | 220.000000 | 220.000000 | 221.000000 | 225.000000 | 225.000000 | 225.000000 | 225.000000 | 225.000000 | 226.000000 | 226.000000 | 227.000000 | 228.000000 | 229.000000 | 232.000000 | 254.000000 | 254.000000 | 254.000000 | 254.000000 | 254.000000 | 254.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 255.000000 | 261.000000 | 261.000000 | 261.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 269.000000 | 270.000000 | 270.000000 | 270.000000 | 270.000000 | 270.000000 | 271.000000 | 271.000000 | 271.000000 | 271.000000 | 272.000000 | 272.000000 | 273.000000 | 273.000000 | 276.000000 | 276.000000 | 283.000000 | 283.000000 | 283.000000 | 283.000000 | 293.000000 | 293.000000 | 314.000000 | 314.000000 | 314.000000 | 317.000000 | 317.000000 | 317.000000 | 317.000000 | 288.000000 | 289.000000 | 289.000000 | 289.000000 | 291.000000 | 291.000000 | 291.000000 | 291.000000 | 295.000000 | 295.000000 | 295.000000 | 296.000000 | 298.000000 | 298.000000 | 298.000000 | 301.000000 | 302.000000 | 304.000000 | 305.000000 | 305.000000 | 306.000000 | 306.000000 | 306.000000 | 306.000000 | 308.000000 | 308.000000 | 308.000000 | 309.000000 | 311.000000 | 313.000000 | 313.000000 | 313.000000 | 312.000000 | 315.000000 | 322.000000 | 322.000000 | 324.000000 | 326.000000 | 329.000000 | 331.000000 | 331.000000 | 335.000000 | 338.000000 | 338.000000 | 346.000000 | 346.000000 | 346.000000 | 346.000000 | 348.000000 | 348.000000 | 348.000000 | 355.000000 | 355.000000 | 349.000000 | 349.000000 | 349.000000 | 349.000000 | 349.000000 | 367.000000 | 367.000000 | 367.000000 | 367.000000 | 377.000000 | 379.000000 | 382.000000 | 385.000000 | 386.000000 | 386.000000 | 392.000000 | 396.000000 | 399.000000 | 422.000000 | 439.000000 | 481.000000 | 508.000000 | 509.000000 | 510.000000 | 523.000000 | 523.000000 | 524.000000 | 525.000000 | 525.000000 | 525.000000 | 526.000000 | 527.000000 | 527.000000 | 529.000000 | 529.000000 | 530.000000 | 530.000000 | 531.000000 | 532.000000 | 534.000000 | 546.000000 | 546.000000 | 554.000000 | 556.000000 | 556.000000 | 556.000000 | 556.000000 | 556.000000 | 556.000000 | 556.000000 | 571.000000 | 571.000000 | 571.000000 | 571.000000 | 571.000000 | 571.000000 | 585.000000 | 585.000000 | 585.000000 | 585.000000 | 585.000000 | 585.000000 | 585.000000 | 585.000000 | 585.000000 | 588.000000 | 592.000000 | 592.000000 | 592.000000 | 598.000000 | 598.000000 | 598.000000 | 598.000000 | 598.000000 | 598.000000 | 622.000000 | 624.000000 | 624.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 660.000000 | 688.000000 | 688.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 699.000000 | 708.000000 | 708.000000 | 708.000000 | 708.000000 | 708.000000 | 709.000000 | 709.000000 | 709.000000 | 709.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 773.000000 | 800.000000 | 872.000000 | 872.000000 | 872.000000 | 872.000000 | 872.000000 | 873.000000 | 873.000000 | 873.000000 | 873.000000 | 873.000000 | 873.000000 | 874.000000 | 874.000000 | 874.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 875.000000 | 882.000000 | 918.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 936.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 977.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 19.584785 | 23.614150 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 2.000000 | 3.000000 | 4.000000 | 6.000000 | 9.000000 | 11.000000 | 14.000000 | 16.000000 | 17.000000 | 26.000000 | 28.000000 | 30.000000 | 35.000000 | 40.000000 | 43.000000 | 48.000000 | 48.000000 | 53.000000 | 66.000000 | 73.000000 | 77.000000 | 84.000000 | 93.000000 | 98.000000 | 108.000000 | 113.000000 | 119.000000 | 131.000000 | 134.000000 | 137.000000 | 143.000000 | 143.000000 | 148.000000 | 153.000000 | 162.000000 | 167.000000 | 167.000000 | 180.000000 | 185.000000 | 186.000000 | 186.000000 | 195.000000 | 197.000000 | 203.000000 | 206.000000 | 220.000000 | 236.000000 | 236.000000 | 246.000000 | 252.000000 | 252.000000 | 263.000000 | 290.000000 | 302.000000 | 303.000000 | 322.000000 | 322.000000 | 322.000000 | 336.000000 | 340.000000 | 365.000000 | 381.000000 | 400.000000 | 415.000000 | 435.000000 | 435.000000 | 435.000000 | 435.000000 | 436.000000 | 436.000000 | 436.000000 | 487.000000 | 488.000000 | 539.000000 | 560.000000 | 561.000000 | 562.000000 | 563.000000 | 577.000000 | 591.000000 | 608.000000 | 610.000000 | 610.000000 | 651.000000 | 651.000000 | 653.000000 | 653.000000 | 669.000000 | 670.000000 | 672.000000 | 674.000000 | 706.000000 | 706.000000 | 781.000000 | 785.000000 | 785.000000 | 800.000000 | 800.000000 | 800.000000 | 800.000000 | 800.000000 | 800.000000 | 802.000000 | 802.000000 | 803.000000 | 803.000000 | 839.000000 | 839.000000 | 839.000000 | 873.000000 | 883.000000 | 887.000000 | 895.000000 | 901.000000 | 900.000000 | 901.000000 | 907.000000 | 918.000000 | 920.000000 | 920.000000 | 931.000000 | 934.000000 | 958.000000 | 967.000000 | 978.000000 | 1005.000000 | 1011.000000 | 1015.000000 | 1032.000000 | 1037.000000 | 1065.000000 | 1069.000000 | 1070.000000 | 1070.000000 | 1070.000000 | 1070.000000 | 1076.000000 | 1076.000000 | 1076.000000 | 1079.000000 | 1136.000000 | 1142.000000 | 1165.000000 | 1165.000000 | 1188.000000 | 1194.000000 | 1199.000000 | 1199.000000 | 1199.000000 | 1248.000000 | 1254.000000 | 1254.000000 | 1268.000000 | 1268.000000 | 1268.000000 | 1288.000000 | 1288.000000 | 1290.000000 | 1290.000000 | 1290.000000 | 1295.000000 | 1315.000000 | 1315.000000 | 1315.000000 | 1424.000000 | 1460.000000 | 1460.000000 | 1490.000000 | 1542.000000 | 1542.000000 | 1566.000000 | 1610.000000 | 1646.000000 | 1714.000000 | 1719.000000 | 1719.000000 | 1753.000000 | 1774.000000 | 1871.000000 | 1954.000000 | 1962.000000 | 1999.000000 | 1999.000000 | 2005.000000 | 2054.000000 | 2161.000000 | 2167.000000 | 2199.000000 | 2215.000000 | 2345.000000 | 2375.000000 | 2375.000000 | 2475.000000 | 2502.000000 | 2506.000000 | 2514.000000 | 2540.000000 | 2543.000000 | 2593.000000 | 2593.000000 | 2615.000000 | 2657.000000 | 2844.000000 | 2844.000000 | 2844.000000 | 2964.000000 | 2964.000000 | 3013.000000 | 3098.000000 | 3166.000000 | 3166.000000 | 3185.000000 | 3463.000000 | 3515.000000 | 3570.000000 | 3585.000000 | 3590.000000 | 3592.000000 | 3595.000000 | 3605.000000 | 3627.000000 | 3734.000000 | 3858.000000 | 3858.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 3697.000000 | 3707.000000 | 3725.000000 | 3777.000000 | 3791.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 3887.000000 | 4034.000000 | 4204.000000 | 4225.000000 | 4225.000000 | 4225.000000 | 4225.000000 | 4267.000000 | 4357.000000 | 4436.000000 | 4530.000000 | 4618.000000 | 4695.000000 | 4733.000000 | 4759.000000 | 4779.000000 | 4827.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4896.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4842.000000 | 4969.000000 | 4988.000000 | 4988.000000 | 4997.000000 | 5048.000000 | 5078.000000 | 5097.000000 | 5110.000000 | 5118.000000 | 5132.000000 | 5149.000000 | 5179.000000 | 5253.000000 | 5259.000000 | 5300.000000 | 5443.000000 | 5474.000000 | 5533.000000 | 5704.000000 | 5710.000000 | 5780.000000 | 5789.000000 | 5801.000000 | 5808.000000 | 5813.000000 | 5816.000000 | 5829.000000 | 5846.000000 | 5846.000000 | 5846.000000 | 5846.000000 | 5846.000000 | 5868.000000 | 5868.000000 | 5868.000000 | 5900.000000 | 5904.000000 | 5915.000000 | 5925.000000 | 5930.000000 | 5937.000000 | 5945.000000 | 5959.000000 | 5970.000000 | 6011.000000 | 6025.000000 | 6037.000000 | 6054.000000 | 6066.000000 | 6071.000000 | 6090.000000 | 6100.000000 | 6931.000000 | 6931.000000 | 6931.000000 | 6931.000000 | 6931.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7019.000000 | 7415.000000 | 7415.000000 | 7415.000000 | 7415.000000 | 7483.000000 | 7483.000000 | 7514.000000 | 7514.000000 | 7514.000000 | 7514.000000 | 7542.000000 | 7544.000000 | 7546.000000 | 7549.000000 | 7552.000000 | 7553.000000 | 7561.000000 | 7571.000000 | 7573.000000 | 7575.000000 | 7757.000000 | 7757.000000 | 7757.000000 | 7757.000000 | 7841.000000 | 8208.000000 | 8208.000000 | 8208.000000 | 8208.000000 | 8208.000000 | 8288.000000 | 8348.000000 | 8368.000000 | 8592.000000 | 8643.000000 | 8875.000000 | 8875.000000 | 9159.000000 | 9303.000000 | 9303.000000 | 9543.000000 | 9543.000000 | 9660.000000 | 9725.000000 | 9781.000000 | 9862.000000 | 9932.000000 | 9978.000000 | 10011.000000 | 10207.000000 | 10250.000000 | 10250.000000 | 10250.000000 | 10312.000000 | 10312.000000 | 10336.000000 | 10368.000000 | 10402.000000 | 10816.000000 | 10866.000000 | 10901.000000 | 10961.000000 | 11006.000000 | 11041.000000 | 11041.000000 | 11041.000000 | 11132.000000 | 11182.000000 | 11221.000000 | 11221.000000 | 11241.000000 | 11266.000000 | 11525.000000 | 11531.000000 | 11535.000000 | 12013.000000 | 12080.000000 | 12085.000000 | 12087.000000 | 12087.000000 | 12087.000000 | 12101.000000 | 12326.000000 | 12376.000000 | 12378.000000 | 12378.000000 | 12378.000000 | 12408.000000 | 12417.000000 | 12428.000000 | 12434.000000 | 12439.000000 | 12439.000000 | 12518.000000 | 12557.000000 | 12557.000000 | 12557.000000 | 12558.000000 | 12560.000000 | 12560.000000 | 12560.000000 | 12563.000000 | 12568.000000 | 12568.000000 | 12590.000000 | 12612.000000 | 12645.000000 | 12645.000000 | 12667.000000 | 13218.000000 | 13218.000000 | 13218.000000 | 13243.000000 | 13252.000000 | 13263.000000 | 13271.000000 | 13284.000000 | 13301.000000 | 13308.000000 | 13308.000000 | 13308.000000 | 13308.000000 | 13313.000000 | 13313.000000 | 13317.000000 | 13317.000000 | 13317.000000 | 13317.000000 | 13317.000000 | 13324.000000 | 13333.000000 | 13333.000000 | 13335.000000 | 13335.000000 | 13340.000000 | 13340.000000 | 13340.000000 | 13350.000000 | 13350.000000 | 13421.000000 | 13964.000000 | 13988.000000 | 13988.000000 | 14077.000000 | 14113.000000 | 14113.000000 | 14180.000000 | 14210.000000 | 14210.000000 | 14210.000000 | 14296.000000 | 14348.000000 | 14380.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 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300.000000 | 338.000000 | 442.000000 | 509.000000 | 510.000000 | 514.000000 | 570.000000 | 577.000000 | 645.000000 | 668.000000 | 707.000000 | 786.000000 | 805.000000 | 866.000000 | 928.000000 | 936.000000 | 965.000000 | 985.000000 | 1015.000000 | 1015.000000 | 1015.000000 | 1057.000000 | 1079.000000 | 1099.000000 | 1254.000000 | 1267.000000 | 1267.000000 | 1277.000000 | 1326.000000 | 1374.000000 | 1417.000000 | 1436.000000 | 1465.000000 | 1496.000000 | 1538.000000 | 1573.000000 | 1596.000000 | 1631.000000 | 1671.000000 | 1690.000000 | 1711.000000 | 1792.000000 | 1792.000000 | 1950.000000 | 2000.000000 | 2030.000000 | 2030.000000 | 2089.000000 | 2160.000000 | 2190.000000 | 2205.000000 | 2276.000000 | 2512.000000 | 2588.000000 | 2699.000000 | 2735.000000 | 2885.000000 | 2987.000000 | 2987.000000 | 2987.000000 | 3113.000000 | 3259.000000 | 3327.000000 | 3411.000000 | 3527.000000 | 3565.000000 | 3630.000000 | 3859.000000 | 3952.000000 | 3959.000000 | 3965.000000 | 3968.000000 | 4155.000000 | 4215.000000 | 4282.000000 | 4296.000000 | 4326.000000 | 4550.000000 | 4606.000000 | 4666.000000 | 4713.000000 | 4858.000000 | 4899.000000 | 5023.000000 | 5097.000000 | 5200.000000 | 5311.000000 | 5381.000000 | 5446.000000 | 5514.000000 | 5580.000000 | 5605.000000 | 5735.000000 | 5809.000000 | 6880.000000 | 6880.000000 | 6844.000000 | 6965.000000 | 7119.000000 | 7276.000000 | 7446.000000 | 7574.000000 | 7743.000000 | 7833.000000 | 7908.000000 | 8071.000000 | 8206.000000 | 8362.000000 | 8495.000000 | 8634.000000 | 8752.000000 | 8954.000000 | 9111.000000 | 9236.000000 | 9415.000000 | 9707.000000 | 10206.000000 | 10411.000000 | 10696.000000 | 11034.000000 | 11428.000000 | 11660.000000 | 12037.000000 | 12359.000000 | 12524.000000 | 12767.000000 | 12921.000000 | 13038.000000 | 13206.000000 | 13332.000000 | 13708.000000 | 14077.000000 | 14368.000000 | 14461.000000 | 14551.000000 | 14765.000000 | 14989.000000 | 15116.000000 | 15205.000000 | 15356.000000 | 15529.000000 | 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168785.000000 | 169070.000000 | 169364.000000 | 169733.000000 | 169733.000000 | 169945.000000 | 170195.000000 | 170489.000000 | 170768.000000 | 171006.000000 | 171279.000000 | 171279.000000 | 171637.000000 | 171874.000000 | 172117.000000 | 172420.000000 | 172637.000000 | 172916.000000 | 172916.000000 | 173189.000000 | 176045.000000 | 176703.000000 | 176881.000000 | 176964.000000 | 177122.000000 | 177122.000000 | 177122.000000 | 177976.000000 | 178136.000000 | 178319.000000 | 179402.000000 | 179858.000000 | 179858.000000 | 179858.000000 | 180245.000000 | 180643.000000 | 180862.000000 | 181199.000000 | 181725.000000 | 181725.000000 | 181725.000000 | 182424.000000 | 182986.000000 | 183306.000000 | 183534.000000 | 183663.000000 | 183663.000000 | 183663.000000 | 186781.000000 | 189163.000000 | 192234.000000 | 195818.000000 | 198981.000000 | 201817.000000 | 204810.000000 | 208361.000000 | 212023.000000 | 215979.000000 | 218022.000000 | 218128.000000 | 218242.000000 | 218331.000000 | 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recovered['Country/Region'].value_counts().head(20)
China 34 United Kingdom 14 France 12 Australia 8 Netherlands 5 Denmark 3 New Zealand 2 Eswatini 1 Andorra 1 Zambia 1 Sweden 1 Slovenia 1 San Marino 1 Sudan 1 North Macedonia 1 Bahrain 1 Cameroon 1 El Salvador 1 Burma 1 Pakistan 1 Name: Country/Region, dtype: int64
recovered.isnull().sum()
Province/State 196
Country/Region 0
Lat 1
Long 1
1/22/20 0
...
5/8/22 0
5/9/22 0
5/10/22 0
5/11/22 0
5/12/22 0
Length: 846, dtype: int64
# record for india for confirmed cases
confirmed['Country/Region']
0 Afghanistan
1 Albania
2 Algeria
3 Andorra
4 Angola
...
279 West Bank and Gaza
280 Winter Olympics 2022
281 Yemen
282 Zambia
283 Zimbabwe
Name: Country/Region, Length: 284, dtype: object
cases = ['Confirmed', 'Deaths', 'Recovered', 'Active']
table['Active'] = table['Confirmed'] - table['Deaths'] - table['Recovered']
table[['Province/State']] = table[['Province/State']].fillna('')
table[cases] = table[cases].fillna(0)
latest = table[table['ObservationDate'] == max(table['ObservationDate'])].reset_index()
latest_grouped = latest['Confirmed'] - latest['Deaths'] - latest['Recovered']
latest_grouped = latest.groupby('Country/Region')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
pred = latest_grouped.sort_values(by='Confirmed', ascending=False)
pred = pred.reset_index(drop=True)
cm = sns.light_palette("red", as_cmap=True)
pred.head(11).style.background_gradient(cmap=cm).background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Blues',subset=["Active"]).background_gradient(cmap='Oranges',subset=["Confirmed"])
temp = table.groupby('ObservationDate')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
temp = temp.sort_values('ObservationDate', ascending=False)
cm = sns.light_palette("red", as_cmap=True)
temp.head(11).style.background_gradient(cmap=cm).background_gradient(cmap='Greens',subset=["Recovered"])\.background_gradient(cmap='Blues',subset=["Active"]).background_gradient(cmap='Oranges',subset=["Confirmed"])
total_count = pd.DataFrame({'Category':'Deaths', 'Count':temp.head(1)['Deaths']})
total_count = total_count.append({'Category':'Recovered','Count':int(temp.head(1)['Recovered'])}, ignore_index=True)
total_count = total_count.append({'Category':"Confirmed",'Count':int(temp.head(1)['Confirmed'])}, ignore_index=True)
total_count = total_count.append({'Category':"Active",'Count':int(temp.head(1)['Active'])}, ignore_index=True)
fig = px.bar(total_count, x='Count', y='Category',
hover_data=['Count'], color='Count',
labels={}, orientation='h',height=400, width = 650)
fig.update_layout(title_text='Confirmed vs Recovered vs Death cases vs Active')
fig.show()
chart=pd.DataFrame()
chart['Country']=df_1.Country.sort_index(ascending = False)
chart['Cases']=df_1.Cases
chart['Active']=df_1.Active
chart['Recovered']=df_1.Recovered
chart['Deaths']=df_1.Deaths
fig = px.bar(chart, x='Cases', y='Country',
hover_data=['Cases'], color='Cases',
labels={},orientation='h', height=800, width=650)
fig.update_layout(title_text='Total number of Confirmed cases')
fig.show()p
fig = px.bar(chart, x='Active', y='Country',
hover_data=['Active'], color='Active',
labels={},orientation='h', height=800, width=650)
fig.update_layout(title_text='10 countries with most Active cases!!')
fig.show()
import numpy as np
X, y = [], []
time_steps = 45
for i in range(len(training_set) - time_steps):
x = training_set_scaled[i:(i+time_steps), 0]
X.append(x)
y.append(training_set_scaled[i+time_steps, 0])
X = np.array(X)
y = np.array(y)
## For model preparation
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Input, LSTM
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import RMSprop
#Total COVID confirmed cases
# df_confirmed = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
#df_confirmed.to_csv('global.csv')
df_confirmed_country = confirmed[confirmed["Country/Region"] == country]
df_confirmed_country = pd.DataFrame(df_confirmed_country[df_confirmed_country.columns[4:]].sum(),columns=["confirmed"])
df_confirmed_country.index = pd.to_datetime(df_confirmed_country.index,format='%m/%d/%y')
df_confirmed_country.plot(figsize=(10,5),title="COVID confirmed cases")
df_confirmed_country.tail(10)
| confirmed | |
|---|---|
| 2022-05-03 | 43088118 |
| 2022-05-04 | 43091393 |
| 2022-05-05 | 43094938 |
| 2022-05-06 | 43098743 |
| 2022-05-07 | 43102194 |
| 2022-05-08 | 43105401 |
| 2022-05-09 | 43107689 |
| 2022-05-10 | 43110586 |
| 2022-05-11 | 43113413 |
| 2022-05-12 | 43116254 |
# total recovered cases in india
df_recovered_country = recovered[recovered["Country/Region"] == country]
df_recovered_country = pd.DataFrame(df_recovered_country[df_recovered_country.columns[4:]].sum(),columns=["Recovered"])
df_recovered_country.index = pd.to_datetime(df_recovered_country.index,format='%m/%d/%y')
df_recovered_country.plot(figsize=(10,5),title="COVID recovered cases")
df_recovered_country.tail(10)
| Recovered | |
|---|---|
| 2022-05-03 | 0 |
| 2022-05-04 | 0 |
| 2022-05-05 | 0 |
| 2022-05-06 | 0 |
| 2022-05-07 | 0 |
| 2022-05-08 | 0 |
| 2022-05-09 | 0 |
| 2022-05-10 | 0 |
| 2022-05-11 | 0 |
| 2022-05-12 | 0 |
# Total Death cases in india
df_death_country = deaths[deaths["Country/Region"] == country]
df_death_country = pd.DataFrame(df_death_country[df_death_country.columns[4:]].sum(),columns=["Deaths"])
df_death_country.index = pd.to_datetime(df_death_country.index,format='%m/%d/%y')
df_death_country.plot(figsize=(10,5),title="COVID Deaths")
df_death_country.tail(10)
| Deaths | |
|---|---|
| 2022-05-03 | 523920 |
| 2022-05-04 | 523975 |
| 2022-05-05 | 524002 |
| 2022-05-06 | 524024 |
| 2022-05-07 | 524064 |
| 2022-05-08 | 524093 |
| 2022-05-09 | 524103 |
| 2022-05-10 | 524157 |
| 2022-05-11 | 524181 |
| 2022-05-12 | 524190 |
print("Total days in the dataset", len(df_confirmed_country))
Total days in the dataset 842
from sklearn.preprocessing import MinMaxScaler
def train_test_split(dataset):
#Use data until 15 days before as training
x = len(dataset)-15
train=dataset.iloc[:x]
test = dataset.iloc[x:]
print(x)
##scale or normalize data as the data is too skewed
scaler = MinMaxScaler()
scaler.fit(train)
train_scaled = scaler.transform(train)
test_scaled = scaler.transform(test)
return train, test, train_scaled, test_scaled
## Use TimeSeriestrain_generator to generate data in sequences.
#Alternatively we can create our own sequences.
from keras.preprocessing.sequence import TimeseriesGenerator
seq_size = 9 ## number of steps (lookback)
n_features = 1 ## number of features. This dataset is univariate so it is 1
def series_generator(seq_size, n_features, train_scaled_confirm, test):
#Sequence size has an impact on prediction, especially since COVID is unpredictable!
train_generator = TimeseriesGenerator(train_scaled, train_scaled, length = seq_size, batch_size=1)
print("Total number of samples in the original training data = ", len(train)) # 271
print("Total number of samples in the generated data = ", len(train_generator)) # 264 with seq_size=7
#Check data shape from generator
x,y = train_generator[10] #Check train_generator
#Takes 7 days as x and 8th day as y (for seq_size=7)
#Also generate test data
test_generator = TimeseriesGenerator(test_scaled, test_scaled, length=seq_size, batch_size=1)
print("Total number of samples in the original training data = ", len(test)) # 14 as we're using last 14 days for test
print("Total number of samples in the generated data = ", len(test_generator)) # 7
#Check data shape from generator
x,y = test_generator[0]
return x,y, test_generator
#Plot the training and validation accuracy and loss at each epoch
def loss_validation_plot(history, title):
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'y', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss for ' + title)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
# Method to predict
def forcast_method(train_scaled, model, test, type_of_data):
#forecast
prediction = [] #Empty list to populate later with predictions
current_batch = train_scaled[-seq_size:] #Final data points in train
current_batch = current_batch.reshape(1, seq_size, n_features) #Reshape
## Predict future, beyond test dates
future = 14 #Days
for i in range(len(test) + future):
current_pred = model.predict(current_batch)[0]
prediction.append(current_pred)
current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)
### Inverse transform to before scaling so we get actual numbers
rescaled_prediction = scaler.inverse_transform(prediction)
time_series_array = test.index #Get dates for test data
#Add new dates for the forecast period
for k in range(0, future):
time_series_array = time_series_array.append(time_series_array[-1:] + pd.DateOffset(1))
#Create a dataframe to capture the forecast data
df_forecast = pd.DataFrame(columns=["actual","predicted"], index=time_series_array)
df_forecast.loc[:,"predicted"] = rescaled_prediction[:,0]
df_forecast.loc[:,"actual"] = test[type_of_data]
#Plot
df_forecast.plot(title="Predictions for next 15 days")
return rescaled_prediction
train_confirm, test_confirm, train_scaled_confirm, test_scaled_confirm = train_test_split(df_confirmed_country)
827
print(train_confirm, '\n -----------------------------------')
print(test_confirm, '\n -----------------------------------')
print(train_scaled_confirm, '\n -----------------------------------')
print(test_scaled_confirm, '\n -----------------------------------')
confirmed
2020-01-22 0
2020-01-23 0
2020-01-24 0
2020-01-25 0
2020-01-26 0
... ...
2022-04-23 43057545
2022-04-24 43060086
2022-04-25 43060097
2022-04-26 43065496
2022-04-27 43068799
[827 rows x 1 columns]
-----------------------------------
confirmed
2022-04-28 43072176
2022-04-29 43075864
2022-04-30 43079188
2022-05-01 43082345
2022-05-02 43084913
2022-05-03 43088118
2022-05-04 43091393
2022-05-05 43094938
2022-05-06 43098743
2022-05-07 43102194
2022-05-08 43105401
2022-05-09 43107689
2022-05-10 43110586
2022-05-11 43113413
2022-05-12 43116254
-----------------------------------
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[9.99459655e-01]
[9.99507648e-01]
[9.99562909e-01]
[9.99619818e-01]
[9.99678491e-01]
[9.99738697e-01]
[9.99797696e-01]
[9.99797951e-01]
[9.99923309e-01]
[1.00000000e+00]]
-----------------------------------
[[1.00007841]
[1.00016404]
[1.00024122]
[1.00031452]
[1.00037415]
[1.00044856]
[1.0005246 ]
[1.00060691]
[1.00069526]
[1.00077539]
[1.00084985]
[1.00090297]
[1.00097024]
[1.00103588]
[1.00110184]]
-----------------------------------
x,y, test_generator= series_generator(seq_size, n_features, train_scaled_confirm, test_confirm)
Total number of samples in the original training data = 827 Total number of samples in the generated data = 818 Total number of samples in the original training data = 15 Total number of samples in the generated data = 6
print(x)
[[[1.00007841] [1.00016404] [1.00024122] [1.00031452] [1.00037415] [1.00044856] [1.0005246 ] [1.00060691] [1.00069526]]]
print(y)
[[1.00077539]]
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Activation
#Define Model
model = Sequential()
model.add(LSTM(32, activation='relu', return_sequences=True, input_shape=(seq_size, n_features)))
model.add(LSTM(32, activation='relu'))
# model.add(Dense(64))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.summary()
Model: "sequential_26" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm_46 (LSTM) (None, 9, 32) 4352 _________________________________________________________________ lstm_47 (LSTM) (None, 32) 8320 _________________________________________________________________ dense_26 (Dense) (None, 1) 33 ================================================================= Total params: 12,705 Trainable params: 12,705 Non-trainable params: 0 _________________________________________________________________
# model = Sequential()
# model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(seq_size, n_features)))
# model.add(LSTM(50, activation='relu'))
# # model.add(Dense(32))
# model.add(Dense(1))
# model.compile(optimizer='adam', loss='mean_squared_error')
# model.summary()
Model: "sequential_22" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm_38 (LSTM) (None, 9, 50) 10400 _________________________________________________________________ lstm_39 (LSTM) (None, 50) 20200 _________________________________________________________________ dense_22 (Dense) (None, 1) 51 ================================================================= Total params: 30,651 Trainable params: 30,651 Non-trainable params: 0 _________________________________________________________________
#Fit the model for confirm cases
##########################
history = model_confirm.fit(train_generator,
validation_data=test_generator,
epochs=25, steps_per_epoch=10)
Epoch 1/25 10/10 [==============================] - 8s 184ms/step - loss: 0.3643 - val_loss: 0.9274 Epoch 2/25 10/10 [==============================] - 0s 13ms/step - loss: 0.1896 - val_loss: 0.7644 Epoch 3/25 10/10 [==============================] - 0s 13ms/step - loss: 0.5023 - val_loss: 0.5551 Epoch 4/25 10/10 [==============================] - 0s 12ms/step - loss: 0.1828 - val_loss: 0.2724 Epoch 5/25 10/10 [==============================] - 0s 14ms/step - loss: 0.0793 - val_loss: 0.0227 Epoch 6/25 10/10 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0387 Epoch 7/25 10/10 [==============================] - 0s 13ms/step - loss: 0.0098 - val_loss: 0.0104 Epoch 8/25 10/10 [==============================] - 0s 15ms/step - loss: 0.0066 - val_loss: 0.0598 Epoch 9/25 10/10 [==============================] - 0s 16ms/step - loss: 0.0200 - val_loss: 0.0018 Epoch 10/25 10/10 [==============================] - 0s 17ms/step - loss: 0.0017 - val_loss: 0.0152 Epoch 11/25 10/10 [==============================] - 0s 20ms/step - loss: 9.5373e-04 - val_loss: 0.0135 Epoch 12/25 10/10 [==============================] - 0s 16ms/step - loss: 0.0059 - val_loss: 0.0110 Epoch 13/25 10/10 [==============================] - 0s 16ms/step - loss: 0.0031 - val_loss: 0.0055 Epoch 14/25 10/10 [==============================] - 0s 16ms/step - loss: 0.0027 - val_loss: 0.0011 Epoch 15/25 10/10 [==============================] - 0s 14ms/step - loss: 8.9680e-04 - val_loss: 2.8829e-07 Epoch 16/25 10/10 [==============================] - 0s 18ms/step - loss: 1.5675e-04 - val_loss: 7.1323e-04 Epoch 17/25 10/10 [==============================] - 0s 15ms/step - loss: 3.0060e-04 - val_loss: 0.0020 Epoch 18/25 10/10 [==============================] - 0s 14ms/step - loss: 6.5091e-04 - val_loss: 8.8491e-04 Epoch 19/25 10/10 [==============================] - 0s 13ms/step - loss: 3.1841e-04 - val_loss: 1.4915e-04 Epoch 20/25 10/10 [==============================] - 0s 16ms/step - loss: 1.2301e-04 - val_loss: 0.0011 Epoch 21/25 10/10 [==============================] - 0s 16ms/step - loss: 5.8313e-05 - val_loss: 0.0013 Epoch 22/25 10/10 [==============================] - 0s 15ms/step - loss: 2.8785e-04 - val_loss: 8.7439e-04 Epoch 23/25 10/10 [==============================] - 0s 13ms/step - loss: 4.6392e-04 - val_loss: 0.0015 Epoch 24/25 10/10 [==============================] - 0s 13ms/step - loss: 5.1001e-05 - val_loss: 0.0016 Epoch 25/25 10/10 [==============================] - 0s 13ms/step - loss: 9.4115e-05 - val_loss: 6.4498e-04
loss_validation_plot(history,'Simple LSTM')
predicted_confirmation = forcast_method(train_scaled_confirm, model, test_confirm, 'confirmed')
predicted_confirmation
array([[5.58523516e+05],
[5.64909879e+05],
[5.45980121e+05],
[5.13836700e+05],
[4.67417919e+05],
[3.98972411e+05],
[3.14403365e+05],
[2.14868154e+05],
[1.12492724e+05],
[7.70788350e+03],
[6.75594419e+03],
[5.66902910e+03],
[4.56581579e+03],
[3.50719650e+03],
[2.51496509e+03],
[1.61620295e+03],
[8.80973497e+02],
[3.48248160e+02],
[7.45451610e+01],
[5.84775631e+01],
[4.39484817e+01],
[3.14611594e+01],
[2.16057637e+01],
[1.34392362e+01],
[7.54898436e+00],
[3.65453828e+00],
[1.38759504e+00],
[5.83800834e-01],
[4.20743465e-01]])
indian_confirm=confirmed[confirmed["Country/Region"] == "India"]
indian_confirm
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | 2/1/20 | 2/2/20 | 2/3/20 | 2/4/20 | 2/5/20 | 2/6/20 | 2/7/20 | 2/8/20 | 2/9/20 | 2/10/20 | 2/11/20 | 2/12/20 | 2/13/20 | 2/14/20 | 2/15/20 | 2/16/20 | 2/17/20 | 2/18/20 | 2/19/20 | 2/20/20 | 2/21/20 | 2/22/20 | 2/23/20 | 2/24/20 | 2/25/20 | 2/26/20 | 2/27/20 | 2/28/20 | 2/29/20 | 3/1/20 | 3/2/20 | 3/3/20 | 3/4/20 | 3/5/20 | 3/6/20 | 3/7/20 | 3/8/20 | 3/9/20 | 3/10/20 | 3/11/20 | 3/12/20 | 3/13/20 | 3/14/20 | 3/15/20 | 3/16/20 | 3/17/20 | 3/18/20 | 3/19/20 | 3/20/20 | 3/21/20 | 3/22/20 | 3/23/20 | 3/24/20 | 3/25/20 | 3/26/20 | 3/27/20 | 3/28/20 | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | 4/8/20 | 4/9/20 | 4/10/20 | 4/11/20 | 4/12/20 | 4/13/20 | 4/14/20 | 4/15/20 | 4/16/20 | 4/17/20 | 4/18/20 | 4/19/20 | 4/20/20 | 4/21/20 | 4/22/20 | 4/23/20 | 4/24/20 | 4/25/20 | 4/26/20 | 4/27/20 | 4/28/20 | 4/29/20 | 4/30/20 | 5/1/20 | 5/2/20 | 5/3/20 | 5/4/20 | 5/5/20 | 5/6/20 | 5/7/20 | 5/8/20 | 5/9/20 | 5/10/20 | 5/11/20 | 5/12/20 | 5/13/20 | 5/14/20 | 5/15/20 | 5/16/20 | 5/17/20 | 5/18/20 | 5/19/20 | 5/20/20 | 5/21/20 | 5/22/20 | 5/23/20 | 5/24/20 | 5/25/20 | 5/26/20 | 5/27/20 | 5/28/20 | 5/29/20 | 5/30/20 | 5/31/20 | 6/1/20 | 6/2/20 | 6/3/20 | 6/4/20 | 6/5/20 | 6/6/20 | 6/7/20 | 6/8/20 | 6/9/20 | 6/10/20 | 6/11/20 | 6/12/20 | 6/13/20 | 6/14/20 | 6/15/20 | 6/16/20 | 6/17/20 | 6/18/20 | 6/19/20 | 6/20/20 | 6/21/20 | 6/22/20 | 6/23/20 | 6/24/20 | 6/25/20 | 6/26/20 | 6/27/20 | 6/28/20 | 6/29/20 | 6/30/20 | 7/1/20 | 7/2/20 | 7/3/20 | 7/4/20 | 7/5/20 | 7/6/20 | 7/7/20 | 7/8/20 | 7/9/20 | 7/10/20 | 7/11/20 | 7/12/20 | 7/13/20 | 7/14/20 | 7/15/20 | 7/16/20 | 7/17/20 | 7/18/20 | 7/19/20 | 7/20/20 | 7/21/20 | 7/22/20 | 7/23/20 | 7/24/20 | 7/25/20 | 7/26/20 | 7/27/20 | 7/28/20 | 7/29/20 | 7/30/20 | 7/31/20 | 8/1/20 | 8/2/20 | 8/3/20 | 8/4/20 | 8/5/20 | 8/6/20 | 8/7/20 | 8/8/20 | 8/9/20 | 8/10/20 | 8/11/20 | 8/12/20 | 8/13/20 | 8/14/20 | 8/15/20 | 8/16/20 | 8/17/20 | 8/18/20 | 8/19/20 | 8/20/20 | 8/21/20 | 8/22/20 | 8/23/20 | 8/24/20 | 8/25/20 | 8/26/20 | 8/27/20 | 8/28/20 | 8/29/20 | 8/30/20 | 8/31/20 | 9/1/20 | 9/2/20 | 9/3/20 | 9/4/20 | 9/5/20 | 9/6/20 | 9/7/20 | 9/8/20 | 9/9/20 | 9/10/20 | 9/11/20 | 9/12/20 | 9/13/20 | 9/14/20 | 9/15/20 | 9/16/20 | 9/17/20 | 9/18/20 | 9/19/20 | 9/20/20 | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | 10/1/20 | 10/2/20 | 10/3/20 | 10/4/20 | 10/5/20 | 10/6/20 | 10/7/20 | 10/8/20 | 10/9/20 | 10/10/20 | 10/11/20 | 10/12/20 | 10/13/20 | 10/14/20 | 10/15/20 | 10/16/20 | 10/17/20 | 10/18/20 | 10/19/20 | 10/20/20 | 10/21/20 | 10/22/20 | 10/23/20 | 10/24/20 | 10/25/20 | 10/26/20 | 10/27/20 | 10/28/20 | 10/29/20 | 10/30/20 | 10/31/20 | 11/1/20 | 11/2/20 | 11/3/20 | 11/4/20 | 11/5/20 | 11/6/20 | 11/7/20 | 11/8/20 | 11/9/20 | 11/10/20 | 11/11/20 | 11/12/20 | 11/13/20 | 11/14/20 | 11/15/20 | 11/16/20 | 11/17/20 | 11/18/20 | 11/19/20 | 11/20/20 | 11/21/20 | 11/22/20 | 11/23/20 | 11/24/20 | 11/25/20 | 11/26/20 | 11/27/20 | 11/28/20 | 11/29/20 | 11/30/20 | 12/1/20 | 12/2/20 | 12/3/20 | 12/4/20 | 12/5/20 | 12/6/20 | 12/7/20 | 12/8/20 | 12/9/20 | 12/10/20 | 12/11/20 | 12/12/20 | 12/13/20 | 12/14/20 | 12/15/20 | 12/16/20 | 12/17/20 | 12/18/20 | 12/19/20 | 12/20/20 | 12/21/20 | 12/22/20 | 12/23/20 | 12/24/20 | 12/25/20 | 12/26/20 | 12/27/20 | 12/28/20 | 12/29/20 | 12/30/20 | 12/31/20 | 1/1/21 | 1/2/21 | 1/3/21 | 1/4/21 | 1/5/21 | 1/6/21 | 1/7/21 | 1/8/21 | 1/9/21 | 1/10/21 | 1/11/21 | 1/12/21 | 1/13/21 | 1/14/21 | 1/15/21 | 1/16/21 | 1/17/21 | 1/18/21 | 1/19/21 | 1/20/21 | 1/21/21 | 1/22/21 | 1/23/21 | 1/24/21 | 1/25/21 | 1/26/21 | 1/27/21 | 1/28/21 | 1/29/21 | 1/30/21 | 1/31/21 | 2/1/21 | 2/2/21 | 2/3/21 | 2/4/21 | 2/5/21 | 2/6/21 | 2/7/21 | 2/8/21 | 2/9/21 | 2/10/21 | 2/11/21 | 2/12/21 | 2/13/21 | 2/14/21 | 2/15/21 | 2/16/21 | 2/17/21 | 2/18/21 | 2/19/21 | 2/20/21 | 2/21/21 | 2/22/21 | 2/23/21 | 2/24/21 | 2/25/21 | 2/26/21 | 2/27/21 | 2/28/21 | 3/1/21 | 3/2/21 | 3/3/21 | 3/4/21 | 3/5/21 | 3/6/21 | 3/7/21 | 3/8/21 | 3/9/21 | 3/10/21 | 3/11/21 | 3/12/21 | 3/13/21 | 3/14/21 | 3/15/21 | 3/16/21 | 3/17/21 | 3/18/21 | 3/19/21 | 3/20/21 | 3/21/21 | 3/22/21 | 3/23/21 | 3/24/21 | 3/25/21 | 3/26/21 | 3/27/21 | 3/28/21 | 3/29/21 | 3/30/21 | 3/31/21 | 4/1/21 | 4/2/21 | 4/3/21 | 4/4/21 | 4/5/21 | 4/6/21 | 4/7/21 | 4/8/21 | 4/9/21 | 4/10/21 | 4/11/21 | 4/12/21 | 4/13/21 | 4/14/21 | 4/15/21 | 4/16/21 | 4/17/21 | 4/18/21 | 4/19/21 | 4/20/21 | 4/21/21 | 4/22/21 | 4/23/21 | 4/24/21 | 4/25/21 | 4/26/21 | 4/27/21 | 4/28/21 | 4/29/21 | 4/30/21 | 5/1/21 | 5/2/21 | 5/3/21 | 5/4/21 | 5/5/21 | 5/6/21 | 5/7/21 | 5/8/21 | 5/9/21 | 5/10/21 | 5/11/21 | 5/12/21 | 5/13/21 | 5/14/21 | 5/15/21 | 5/16/21 | 5/17/21 | 5/18/21 | 5/19/21 | 5/20/21 | 5/21/21 | 5/22/21 | 5/23/21 | 5/24/21 | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | 5/30/21 | 5/31/21 | 6/1/21 | 6/2/21 | 6/3/21 | 6/4/21 | 6/5/21 | 6/6/21 | 6/7/21 | 6/8/21 | 6/9/21 | 6/10/21 | 6/11/21 | 6/12/21 | 6/13/21 | 6/14/21 | 6/15/21 | 6/16/21 | 6/17/21 | 6/18/21 | 6/19/21 | 6/20/21 | 6/21/21 | 6/22/21 | 6/23/21 | 6/24/21 | 6/25/21 | 6/26/21 | 6/27/21 | 6/28/21 | 6/29/21 | 6/30/21 | 7/1/21 | 7/2/21 | 7/3/21 | 7/4/21 | 7/5/21 | 7/6/21 | 7/7/21 | 7/8/21 | 7/9/21 | 7/10/21 | 7/11/21 | 7/12/21 | 7/13/21 | 7/14/21 | 7/15/21 | 7/16/21 | 7/17/21 | 7/18/21 | 7/19/21 | 7/20/21 | 7/21/21 | 7/22/21 | 7/23/21 | 7/24/21 | 7/25/21 | 7/26/21 | 7/27/21 | 7/28/21 | 7/29/21 | 7/30/21 | 7/31/21 | 8/1/21 | 8/2/21 | 8/3/21 | 8/4/21 | 8/5/21 | 8/6/21 | 8/7/21 | 8/8/21 | 8/9/21 | 8/10/21 | 8/11/21 | 8/12/21 | 8/13/21 | 8/14/21 | 8/15/21 | 8/16/21 | 8/17/21 | 8/18/21 | 8/19/21 | 8/20/21 | 8/21/21 | 8/22/21 | 8/23/21 | 8/24/21 | 8/25/21 | 8/26/21 | 8/27/21 | 8/28/21 | 8/29/21 | 8/30/21 | 8/31/21 | 9/1/21 | 9/2/21 | 9/3/21 | 9/4/21 | 9/5/21 | 9/6/21 | 9/7/21 | 9/8/21 | 9/9/21 | 9/10/21 | 9/11/21 | 9/12/21 | 9/13/21 | 9/14/21 | 9/15/21 | 9/16/21 | 9/17/21 | 9/18/21 | 9/19/21 | 9/20/21 | 9/21/21 | 9/22/21 | 9/23/21 | 9/24/21 | 9/25/21 | 9/26/21 | 9/27/21 | 9/28/21 | 9/29/21 | 9/30/21 | 10/1/21 | 10/2/21 | 10/3/21 | 10/4/21 | 10/5/21 | 10/6/21 | 10/7/21 | 10/8/21 | 10/9/21 | 10/10/21 | 10/11/21 | 10/12/21 | 10/13/21 | 10/14/21 | 10/15/21 | 10/16/21 | 10/17/21 | 10/18/21 | 10/19/21 | 10/20/21 | 10/21/21 | 10/22/21 | 10/23/21 | 10/24/21 | 10/25/21 | 10/26/21 | 10/27/21 | 10/28/21 | 10/29/21 | 10/30/21 | 10/31/21 | 11/1/21 | 11/2/21 | 11/3/21 | 11/4/21 | 11/5/21 | 11/6/21 | 11/7/21 | 11/8/21 | 11/9/21 | 11/10/21 | 11/11/21 | 11/12/21 | 11/13/21 | 11/14/21 | 11/15/21 | 11/16/21 | 11/17/21 | 11/18/21 | 11/19/21 | 11/20/21 | 11/21/21 | 11/22/21 | 11/23/21 | 11/24/21 | 11/25/21 | 11/26/21 | 11/27/21 | 11/28/21 | 11/29/21 | 11/30/21 | 12/1/21 | 12/2/21 | 12/3/21 | 12/4/21 | 12/5/21 | 12/6/21 | 12/7/21 | 12/8/21 | 12/9/21 | 12/10/21 | 12/11/21 | 12/12/21 | 12/13/21 | 12/14/21 | 12/15/21 | 12/16/21 | 12/17/21 | 12/18/21 | 12/19/21 | 12/20/21 | 12/21/21 | 12/22/21 | 12/23/21 | 12/24/21 | 12/25/21 | 12/26/21 | 12/27/21 | 12/28/21 | 12/29/21 | 12/30/21 | 12/31/21 | 1/1/22 | 1/2/22 | 1/3/22 | 1/4/22 | 1/5/22 | 1/6/22 | 1/7/22 | 1/8/22 | 1/9/22 | 1/10/22 | 1/11/22 | 1/12/22 | 1/13/22 | 1/14/22 | 1/15/22 | 1/16/22 | 1/17/22 | 1/18/22 | 1/19/22 | 1/20/22 | 1/21/22 | 1/22/22 | 1/23/22 | 1/24/22 | 1/25/22 | 1/26/22 | 1/27/22 | 1/28/22 | 1/29/22 | 1/30/22 | 1/31/22 | 2/1/22 | 2/2/22 | 2/3/22 | 2/4/22 | 2/5/22 | 2/6/22 | 2/7/22 | 2/8/22 | 2/9/22 | 2/10/22 | 2/11/22 | 2/12/22 | 2/13/22 | 2/14/22 | 2/15/22 | 2/16/22 | 2/17/22 | 2/18/22 | 2/19/22 | 2/20/22 | 2/21/22 | 2/22/22 | 2/23/22 | 2/24/22 | 2/25/22 | 2/26/22 | 2/27/22 | 2/28/22 | 3/1/22 | 3/2/22 | 3/3/22 | 3/4/22 | 3/5/22 | 3/6/22 | 3/7/22 | 3/8/22 | 3/9/22 | 3/10/22 | 3/11/22 | 3/12/22 | 3/13/22 | 3/14/22 | 3/15/22 | 3/16/22 | 3/17/22 | 3/18/22 | 3/19/22 | 3/20/22 | 3/21/22 | 3/22/22 | 3/23/22 | 3/24/22 | 3/25/22 | 3/26/22 | 3/27/22 | 3/28/22 | 3/29/22 | 3/30/22 | 3/31/22 | 4/1/22 | 4/2/22 | 4/3/22 | 4/4/22 | 4/5/22 | 4/6/22 | 4/7/22 | 4/8/22 | 4/9/22 | 4/10/22 | 4/11/22 | 4/12/22 | 4/13/22 | 4/14/22 | 4/15/22 | 4/16/22 | 4/17/22 | 4/18/22 | 4/19/22 | 4/20/22 | 4/21/22 | 4/22/22 | 4/23/22 | 4/24/22 | 4/25/22 | 4/26/22 | 4/27/22 | 4/28/22 | 4/29/22 | 4/30/22 | 5/1/22 | 5/2/22 | 5/3/22 | 5/4/22 | 5/5/22 | 5/6/22 | 5/7/22 | 5/8/22 | 5/9/22 | 5/10/22 | 5/11/22 | 5/12/22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 148 | NaN | India | 20.593684 | 78.96288 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 5 | 5 | 28 | 30 | 31 | 34 | 39 | 43 | 56 | 62 | 73 | 82 | 102 | 113 | 119 | 142 | 156 | 194 | 244 | 330 | 396 | 499 | 536 | 657 | 727 | 887 | 987 | 1024 | 1251 | 1397 | 1998 | 2543 | 2567 | 3082 | 3588 | 4778 | 5311 | 5916 | 6725 | 7598 | 8446 | 9205 | 10453 | 11487 | 12322 | 13430 | 14352 | 15722 | 17615 | 18539 | 20080 | 21370 | 23077 | 24530 | 26283 | 27890 | 29451 | 31324 | 33062 | 34863 | 37257 | 39699 | 42505 | 46437 | 49400 | 52987 | 56351 | 59695 | 62808 | 67161 | 70768 | 74292 | 78055 | 81997 | 85784 | 90648 | 95698 | 100328 | 106475 | 112028 | 118226 | 124794 | 131423 | 138536 | 144950 | 150793 | 158086 | 165386 | 173491 | 181827 | 190609 | 198370 | 207191 | 216824 | 226713 | 236184 | 246622 | 257486 | 265928 | 276146 | 286605 | 297535 | 308993 | 320922 | 332424 | 343091 | 354065 | 366946 | 380532 | 395048 | 410451 | 425282 | 440215 | 456183 | 473105 | 490401 | 508953 | 528859 | 548318 | 566840 | 585481 | 604641 | 625544 | 648315 | 673165 | 697413 | 719664 | 742417 | 767296 | 793802 | 820916 | 849522 | 878254 | 906752 | 936181 | 968857 | 1003832 | 1039084 | 1077781 | 1118206 | 1155338 | 1193078 | 1238798 | 1288108 | 1337024 | 1385635 | 1435616 | 1480073 | 1531669 | 1581963 | 1634746 | 1695988 | 1750723 | 1803695 | 1855745 | 1908254 | 1964536 | 2027074 | 2088611 | 2153010 | 2215074 | 2268675 | 2329638 | 2396637 | 2461190 | 2525922 | 2589952 | 2647663 | 2702681 | 2767253 | 2836925 | 2905825 | 2975701 | 3044940 | 3106348 | 3167323 | 3224547 | 3310234 | 3387500 | 3463972 | 3542733 | 3621245 | 3691166 | 3769523 | 3853406 | 3936747 | 4023179 | 4113811 | 4204613 | 4280422 | 4370128 | 4465863 | 4562414 | 4659984 | 4754356 | 4846427 | 4930236 | 5020359 | 5118253 | 5214677 | 5308014 | 5400619 | 5487580 | 5562663 | 5646010 | 5732518 | 5818570 | 5903932 | 5992532 | 6074702 | 6145291 | 6225763 | 6312584 | 6394068 | 6473544 | 6549373 | 6623815 | 6685082 | 6757131 | 6835655 | 6906151 | 6979423 | 7053806 | 7120538 | 7175880 | 7239389 | 7307097 | 7370468 | 7432680 | 7494551 | 7550273 | 7597063 | 7651107 | 7706946 | 7761312 | 7814682 | 7864811 | 7909959 | 7946429 | 7990322 | 8040203 | 8088851 | 8137119 | 8184082 | 8229313 | 8267623 | 8313876 | 8364086 | 8411724 | 8462080 | 8507754 | 8553657 | 8591730 | 8636011 | 8683916 | 8728795 | 8773479 | 8814579 | 8845127 | 8874290 | 8912907 | 8958483 | 9004365 | 9050597 | 9095806 | 9139865 | 9177840 | 9222216 | 9266705 | 9309787 | 9351109 | 9392919 | 9431691 | 9462809 | 9499413 | 9534964 | 9571559 | 9608211 | 9644222 | 9677203 | 9703770 | 9735850 | 9767371 | 9796744 | 9826775 | 9857029 | 9884100 | 9906165 | 9932547 | 9956557 | 9979447 | 10004599 | 10031223 | 10055560 | 10075116 | 10099066 | 10123778 | 10146845 | 10169118 | 10187850 | 10207871 | 10224303 | 10244852 | 10266674 | 10286709 | 10305788 | 10323965 | 10340469 | 10356844 | 10374932 | 10395278 | 10413417 | 10413417 | 10450284 | 10466595 | 10479179 | 10495147 | 10512093 | 10527683 | 10542841 | 10557985 | 10571773 | 10581823 | 10595639 | 10610883 | 10625428 | 10639684 | 10654533 | 10667736 | 10676838 | 10689527 | 10701193 | 10720048 | 10733130 | 10746174 | 10757610 | 10766245 | 10777284 | 10790183 | 10802591 | 10814304 | 10826363 | 10838194 | 10847304 | 10858371 | 10871294 | 10880603 | 10892746 | 10904940 | 10916589 | 10925710 | 10937320 | 10950201 | 10963394 | 10977387 | 10991651 | 11005850 | 11016434 | 11030176 | 11046914 | 11063491 | 11079979 | 11096731 | 11112241 | 11124527 | 11139516 | 11156923 | 11173761 | 11192045 | 11210799 | 11229398 | 11244786 | 11262707 | 11285561 | 11308846 | 11333728 | 11359048 | 11385339 | 11409831 | 11438734 | 11474605 | 11514331 | 11555284 | 11599130 | 11646081 | 11686796 | 11734058 | 11787534 | 11846652 | 11908910 | 11971624 | 12039644 | 12095855 | 12149335 | 12221665 | 12303131 | 12392260 | 12485509 | 12589067 | 12686049 | 12801785 | 12928574 | 13060542 | 13205926 | 13358805 | 13527717 | 13689453 | 13873825 | 14074564 | 14291917 | 14526609 | 14788003 | 15061805 | 15320972 | 15616130 | 15930774 | 16263695 | 16610481 | 16960172 | 17313163 | 17636186 | 17997113 | 18376421 | 18762976 | 19164969 | 19557457 | 19925517 | 20282833 | 20664979 | 21077410 | 21491598 | 21892676 | 22296081 | 22662575 | 22992517 | 23340938 | 23703665 | 24046809 | 24372907 | 24684077 | 24965463 | 25228996 | 25496330 | 25772440 | 26031991 | 26289290 | 26530132 | 26752447 | 26948874 | 27157795 | 27369093 | 27555457 | 27729247 | 27894800 | 28047534 | 28175044 | 28307832 | 28441986 | 28574350 | 28694879 | 28809339 | 28909975 | 28996473 | 29089069 | 29182532 | 29274823 | 29359155 | 29439989 | 29510410 | 29570881 | 29633105 | 29700313 | 29762793 | 29823546 | 29881772 | 29935221 | 29977861 | 30028709 | 30082778 | 30134445 | 30183143 | 30233183 | 30279331 | 30316897 | 30362848 | 30411634 | 30458251 | 30502362 | 30545433 | 30585229 | 30619932 | 30663665 | 30709557 | 30752950 | 30795716 | 30837222 | 30874376 | 30907282 | 30946147 | 30987880 | 31026829 | 31064908 | 31106065 | 31144229 | 31174322 | 31216337 | 31257720 | 31293062 | 31332159 | 31371901 | 31411262 | 31440951 | 31484605 | 31528114 | 31572344 | 31613993 | 31655824 | 31695958 | 31726507 | 31769132 | 31812114 | 31856757 | 31895385 | 31934455 | 31969954 | 31998158 | 32036511 | 32077706 | 32117826 | 32156493 | 32192576 | 32225513 | 32250679 | 32285857 | 32322258 | 32358829 | 32393286 | 32424234 | 32449306 | 32474773 | 32512366 | 32558530 | 32603188 | 32649947 | 32695030 | 32737939 | 32768880 | 32810845 | 32857937 | 32903289 | 32945907 | 32988673 | 33027621 | 33058843 | 33096718 | 33139981 | 33174954 | 33208330 | 33236921 | 33264175 | 33289579 | 33316755 | 33347325 | 33381728 | 33417390 | 33448163 | 33478419 | 33504534 | 33531498 | 33563421 | 33594803 | 33624419 | 33652745 | 33678786 | 33697581 | 33716451 | 33739980 | 33766707 | 33791061 | 33813903 | 33834702 | 33853048 | 33871881 | 33894312 | 33915569 | 33935309 | 33953475 | 33971607 | 33985920 | 34001743 | 34020730 | 34037592 | 34053573 | 34067719 | 34081315 | 34094373 | 34108996 | 34127450 | 34143236 | 34159562 | 34175468 | 34189774 | 34202202 | 34215653 | 34231809 | 34246157 | 34260470 | 34273300 | 34285814 | 34296237 | 34308140 | 34321025 | 34333754 | 34344683 | 34355509 | 34366987 | 34377113 | 34388579 | 34401670 | 34414186 | 34426036 | 34437307 | 34447536 | 34456401 | 34466598 | 34478517 | 34489623 | 34499925 | 34510413 | 34518901 | 34526480 | 34535763 | 34544882 | 34555431 | 34563749 | 34572523 | 34580832 | 34587822 | 34596776 | 34606541 | 34615757 | 34624360 | 34624360 | 34641561 | 34648383 | 34656822 | 34666241 | 34674643 | 34682633 | 34690510 | 34697860 | 34703644 | 34710628 | 34718602 | 34726049 | 34726049 | 34733194 | 34746838 | 34752164 | 34758481 | 34765976 | 34772626 | 34779815 | 34786802 | 34793333 | 34799691 | 34808886 | 34822040 | 34838804 | 34861579 | 34889132 | 34922882 | 34960261 | 35018358 | 35109286 | 35226386 | 35368372 | 35528004 | 35707727 | 35875790 | 36070510 | 36317927 | 36582129 | 36850962 | 37122164 | 37380253 | 37618271 | 37901241 | 38218773 | 38566027 | 38903731 | 39237264 | 39543328 | 39799202 | 40085116 | 40371500 | 40622709 | 40858241 | 41092522 | 41302440 | 41469499 | 41630885 | 41803318 | 41952712 | 42080664 | 42188138 | 42272014 | 42339611 | 42410976 | 42478060 | 42536137 | 42586544 | 42631421 | 42665534 | 42692943 | 42723558 | 42754315 | 42780235 | 42802505 | 42822473 | 42838524 | 42851929 | 42867031 | 42881179 | 42894345 | 42905844 | 42916117 | 42924130 | 42931045 | 42938599 | 42945160 | 42951556 | 42957477 | 42962953 | 42967315 | 42971308 | 42975883 | 42980067 | 42984261 | 42987875 | 42990991 | 42993494 | 42996062 | 42998938 | 43001477 | 43004005 | 43006080 | 43007841 | 43009390 | 43010971 | 43012749 | 43014687 | 43016372 | 43018032 | 43019453 | 43020723 | 43021982 | 43023215 | 43024440 | 43025775 | 43027035 | 43028131 | 43029044 | 43029839 | 43030925 | 43031958 | 43033067 | 43034217 | 43035271 | 43036132 | 43036928 | 43038016 | 43039023 | 43039972 | 43040947 | 43042097 | 43044280 | 43045527 | 43047594 | 43049974 | 43052425 | 43054952 | 43057545 | 43060086 | 43060097 | 43065496 | 43068799 | 43072176 | 43075864 | 43079188 | 43082345 | 43084913 | 43088118 | 43091393 | 43094938 | 43098743 | 43102194 | 43105401 | 43107689 | 43110586 | 43113413 | 43116254 |
indian_confirm.drop(['Lat','Long','Province/State'], axis = 1)
#Split and normalize data for covid recovered data
train_recover, test_recover, train_scaled_recover, test_scaled_recover = train_test_split(df_recover_country)
827
df_recovered_country
| Recovered | |
|---|---|
| 2020-01-22 | 0 |
| 2020-01-23 | 0 |
| 2020-01-24 | 0 |
| 2020-01-25 | 0 |
| 2020-01-26 | 0 |
| ... | ... |
| 2022-05-08 | 0 |
| 2022-05-09 | 0 |
| 2022-05-10 | 0 |
| 2022-05-11 | 0 |
| 2022-05-12 | 0 |
842 rows × 1 columns
print(train_recover, '\n -----------------------------------')
print(test_recover, '\n -----------------------------------')
print(train_scaled_recover, '\n -----------------------------------')
print(test_scaled_recover, '\n -----------------------------------')
Recovered
2020-01-22 0
2020-01-23 0
2020-01-24 0
2020-01-25 0
2020-01-26 0
... ...
2022-04-23 0
2022-04-24 0
2022-04-25 0
2022-04-26 0
2022-04-27 0
[827 rows x 1 columns]
-----------------------------------
Recovered
2022-04-28 0
2022-04-29 0
2022-04-30 0
2022-05-01 0
2022-05-02 0
2022-05-03 0
2022-05-04 0
2022-05-05 0
2022-05-06 0
2022-05-07 0
2022-05-08 0
2022-05-09 0
2022-05-10 0
2022-05-11 0
2022-05-12 0
-----------------------------------
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-----------------------------------
[[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]
[0.]]
-----------------------------------
x,y, test_generator= series_generator(seq_size, n_features, train_scaled_recover, test_recover)
Total number of samples in the original training data = 827 Total number of samples in the generated data = 818 Total number of samples in the original training data = 15 Total number of samples in the generated data = 6
#Fit the model for recovered cases
##########################
history = model_confirm.fit(train_generator,
validation_data=test_generator,
epochs=25, steps_per_epoch=10)
Epoch 1/25 10/10 [==============================] - 0s 10ms/step - loss: 1.5950e-04 - val_loss: 1.7015e-04 Epoch 2/25 10/10 [==============================] - 0s 9ms/step - loss: 1.0588e-04 - val_loss: 6.0207e-05 Epoch 3/25 10/10 [==============================] - 0s 9ms/step - loss: 8.8822e-05 - val_loss: 2.1301e-07 Epoch 4/25 10/10 [==============================] - 0s 8ms/step - loss: 1.0243e-04 - val_loss: 6.0809e-04 Epoch 5/25 10/10 [==============================] - 0s 9ms/step - loss: 1.2446e-04 - val_loss: 1.1784e-04 Epoch 6/25 10/10 [==============================] - 0s 9ms/step - loss: 3.1580e-04 - val_loss: 0.0017 Epoch 7/25 10/10 [==============================] - 0s 9ms/step - loss: 1.7628e-04 - val_loss: 8.5124e-06 Epoch 8/25 10/10 [==============================] - 0s 9ms/step - loss: 2.0117e-04 - val_loss: 7.0201e-04 Epoch 9/25 10/10 [==============================] - 0s 10ms/step - loss: 1.7443e-04 - val_loss: 7.0029e-04 Epoch 10/25 10/10 [==============================] - 0s 10ms/step - loss: 3.5701e-04 - val_loss: 4.1393e-04 Epoch 11/25 10/10 [==============================] - 0s 8ms/step - loss: 2.7294e-04 - val_loss: 1.7846e-04 Epoch 12/25 10/10 [==============================] - 0s 9ms/step - loss: 3.7671e-05 - val_loss: 6.8496e-06 Epoch 13/25 10/10 [==============================] - 0s 10ms/step - loss: 7.8295e-05 - val_loss: 4.4614e-04 Epoch 14/25 10/10 [==============================] - 0s 10ms/step - loss: 1.0416e-04 - val_loss: 2.7272e-05 Epoch 15/25 10/10 [==============================] - 0s 9ms/step - loss: 3.6577e-05 - val_loss: 1.9421e-04 Epoch 16/25 10/10 [==============================] - 0s 11ms/step - loss: 7.6750e-05 - val_loss: 8.0149e-04 Epoch 17/25 10/10 [==============================] - 0s 8ms/step - loss: 3.0345e-04 - val_loss: 1.2941e-04 Epoch 18/25 10/10 [==============================] - 0s 8ms/step - loss: 2.0541e-04 - val_loss: 6.5417e-04 Epoch 19/25 10/10 [==============================] - 0s 9ms/step - loss: 2.8453e-04 - val_loss: 2.4653e-04 Epoch 20/25 10/10 [==============================] - 0s 10ms/step - loss: 1.0034e-04 - val_loss: 2.8019e-04 Epoch 21/25 10/10 [==============================] - 0s 9ms/step - loss: 2.6099e-04 - val_loss: 7.0947e-04 Epoch 22/25 10/10 [==============================] - 0s 9ms/step - loss: 4.3895e-04 - val_loss: 2.7352e-04 Epoch 23/25 10/10 [==============================] - 0s 9ms/step - loss: 1.1270e-04 - val_loss: 2.5545e-04 Epoch 24/25 10/10 [==============================] - 0s 9ms/step - loss: 2.3358e-05 - val_loss: 2.9850e-06 Epoch 25/25 10/10 [==============================] - 0s 9ms/step - loss: 4.7784e-05 - val_loss: 3.3335e-04
loss_validation_plot(history,'Simple LSTM')
test_recover
| Recovered | |
|---|---|
| 2022-04-28 | 0 |
| 2022-04-29 | 0 |
| 2022-04-30 | 0 |
| 2022-05-01 | 0 |
| 2022-05-02 | 0 |
| 2022-05-03 | 0 |
| 2022-05-04 | 0 |
| 2022-05-05 | 0 |
| 2022-05-06 | 0 |
| 2022-05-07 | 0 |
| 2022-05-08 | 0 |
| 2022-05-09 | 0 |
| 2022-05-10 | 0 |
| 2022-05-11 | 0 |
| 2022-05-12 | 0 |
recovery_predicted=forcast_method(train_scaled_recover, model, test_recover, 'Recovered')
train_death, test_death, train_scaled_death, test_scaled_death = train_test_split(df_death_country)
827
print(train_death, '\n -----------------------------------')
print(test_death, '\n -----------------------------------')
print(train_scaled_death, '\n -----------------------------------')
print(test_scaled_death, '\n -----------------------------------')
Deaths
2020-01-22 0
2020-01-23 0
2020-01-24 0
2020-01-25 0
2020-01-26 0
... ...
2022-04-23 522193
2022-04-24 522223
2022-04-25 522223
2022-04-26 523654
2022-04-27 523693
[827 rows x 1 columns]
-----------------------------------
Deaths
2022-04-28 523753
2022-04-29 523803
2022-04-30 523843
2022-05-01 523869
2022-05-02 523889
2022-05-03 523920
2022-05-04 523975
2022-05-05 524002
2022-05-06 524024
2022-05-07 524064
2022-05-08 524093
2022-05-09 524103
2022-05-10 524157
2022-05-11 524181
2022-05-12 524190
-----------------------------------
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-----------------------------------
[[1.00011457]
[1.00021005]
[1.00028643]
[1.00033607]
[1.00037427]
[1.00043346]
[1.00053848]
[1.00059004]
[1.00063205]
[1.00070843]
[1.00076381]
[1.0007829 ]
[1.00088602]
[1.00093184]
[1.00094903]]
-----------------------------------
x,y, test_generator= series_generator(seq_size, n_features, train_scaled_death, test_death)
Total number of samples in the original training data = 827 Total number of samples in the generated data = 818 Total number of samples in the original training data = 15 Total number of samples in the generated data = 6
history = model_confirm.fit(train_generator,
validation_data=test_generator,
epochs=25, steps_per_epoch=10)
Epoch 1/25 10/10 [==============================] - 0s 10ms/step - loss: 9.2465e-05 - val_loss: 1.2258e-04 Epoch 2/25 10/10 [==============================] - 0s 10ms/step - loss: 1.2039e-04 - val_loss: 3.7233e-04 Epoch 3/25 10/10 [==============================] - 0s 8ms/step - loss: 1.4917e-04 - val_loss: 3.1314e-05 Epoch 4/25 10/10 [==============================] - 0s 8ms/step - loss: 6.6142e-05 - val_loss: 6.2657e-05 Epoch 5/25 10/10 [==============================] - 0s 8ms/step - loss: 1.4593e-04 - val_loss: 0.0011 Epoch 6/25 10/10 [==============================] - 0s 8ms/step - loss: 2.4252e-04 - val_loss: 9.5580e-06 Epoch 7/25 10/10 [==============================] - 0s 9ms/step - loss: 1.4041e-04 - val_loss: 2.6089e-04 Epoch 8/25 10/10 [==============================] - 0s 9ms/step - loss: 3.5435e-05 - val_loss: 1.7877e-06 Epoch 9/25 10/10 [==============================] - 0s 9ms/step - loss: 3.3125e-05 - val_loss: 2.9287e-04 Epoch 10/25 10/10 [==============================] - 0s 11ms/step - loss: 5.9444e-05 - val_loss: 1.0731e-04 Epoch 11/25 10/10 [==============================] - 0s 9ms/step - loss: 6.1967e-05 - val_loss: 2.0062e-04 Epoch 12/25 10/10 [==============================] - 0s 9ms/step - loss: 8.1470e-05 - val_loss: 5.4403e-05 Epoch 13/25 10/10 [==============================] - 0s 9ms/step - loss: 1.2529e-04 - val_loss: 6.5215e-04 Epoch 14/25 10/10 [==============================] - 0s 10ms/step - loss: 8.0724e-05 - val_loss: 5.6386e-05 Epoch 15/25 10/10 [==============================] - 0s 10ms/step - loss: 7.2505e-05 - val_loss: 0.0014 Epoch 16/25 10/10 [==============================] - 0s 9ms/step - loss: 1.7620e-04 - val_loss: 9.6120e-07 Epoch 17/25 10/10 [==============================] - 0s 8ms/step - loss: 2.2443e-04 - val_loss: 8.8694e-04 Epoch 18/25 10/10 [==============================] - 0s 8ms/step - loss: 1.8154e-04 - val_loss: 3.8967e-04 Epoch 19/25 10/10 [==============================] - 0s 9ms/step - loss: 1.1934e-04 - val_loss: 1.5915e-05 Epoch 20/25 10/10 [==============================] - 0s 9ms/step - loss: 1.5736e-04 - val_loss: 6.3871e-08 Epoch 21/25 10/10 [==============================] - 0s 11ms/step - loss: 1.3132e-04 - val_loss: 1.0603e-04 Epoch 22/25 10/10 [==============================] - 0s 8ms/step - loss: 4.1191e-05 - val_loss: 1.7356e-05 Epoch 23/25 10/10 [==============================] - 0s 9ms/step - loss: 4.2220e-05 - val_loss: 2.4188e-05 Epoch 24/25 10/10 [==============================] - 0s 8ms/step - loss: 9.2544e-05 - val_loss: 0.0014 Epoch 25/25 10/10 [==============================] - 0s 9ms/step - loss: 1.5172e-04 - val_loss: 3.5490e-04
loss_validation_plot(history,'Simple LSTM')
test_death
| Deaths | |
|---|---|
| 2022-04-28 | 523753 |
| 2022-04-29 | 523803 |
| 2022-04-30 | 523843 |
| 2022-05-01 | 523869 |
| 2022-05-02 | 523889 |
| 2022-05-03 | 523920 |
| 2022-05-04 | 523975 |
| 2022-05-05 | 524002 |
| 2022-05-06 | 524024 |
| 2022-05-07 | 524064 |
| 2022-05-08 | 524093 |
| 2022-05-09 | 524103 |
| 2022-05-10 | 524157 |
| 2022-05-11 | 524181 |
| 2022-05-12 | 524190 |
death_predicted = forcast_method(train_scaled_death, model, test_death, 'Deaths')
death_predicted
array([[5.57347892e+05],
[5.63714800e+05],
[5.44877144e+05],
[5.12856954e+05],
[4.66605569e+05],
[3.98373387e+05],
[3.14072986e+05],
[2.14849661e+05],
[1.12470230e+05],
[7.69392129e+03],
[6.74432238e+03],
[5.65992571e+03],
[4.55925714e+03],
[3.50297825e+03],
[2.51252768e+03],
[1.61512425e+03],
[8.80749658e+02],
[3.48082023e+02],
[7.44292992e+01],
[5.83946026e+01],
[4.38906561e+01],
[3.14265885e+01],
[2.15851508e+01],
[1.34286690e+01],
[7.54445304e+00],
[3.65285901e+00],
[1.38643481e+00],
[5.83025237e-01],
[4.20234395e-01]])
#The LSTM Model 2
model2 = Sequential()
# model2.add(Input(shape=(1, time_steps)))
model2.add(LSTM(48, return_sequences=True,input_shape=(seq_size, n_features)))
model2.add(Dropout(0.4))
model2.add(LSTM(48, return_sequences=True))
model2.add(Dropout(0.2))
model2.add(LSTM(48))
model2.add(Dropout(0.2))
model2.add(Dense(1, activation='relu'))
model2.compile(loss = 'mean_squared_error',
optimizer = 'adam',
metrics = ['mean_squared_error'])
model2.summary()
Model: "sequential_11" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm_15 (LSTM) (None, 7, 48) 9600 _________________________________________________________________ dropout_3 (Dropout) (None, 7, 48) 0 _________________________________________________________________ lstm_16 (LSTM) (None, 7, 48) 18624 _________________________________________________________________ dropout_4 (Dropout) (None, 7, 48) 0 _________________________________________________________________ lstm_17 (LSTM) (None, 48) 18624 _________________________________________________________________ dropout_5 (Dropout) (None, 48) 0 _________________________________________________________________ dense_12 (Dense) (None, 1) 49 ================================================================= Total params: 46,897 Trainable params: 46,897 Non-trainable params: 0 _________________________________________________________________
#Fit the model
##########################
epoch = 48
history2 = model2.fit(train_generator,
validation_data=test_generator,
epochs=epoch, steps_per_epoch=10)
Epoch 1/48 10/10 [==============================] - 28s 599ms/step - loss: 0.1172 - mean_squared_error: 0.1172 - val_loss: 0.5097 - val_mean_squared_error: 0.5097 Epoch 2/48 10/10 [==============================] - 0s 33ms/step - loss: 0.1376 - mean_squared_error: 0.1376 - val_loss: 0.0283 - val_mean_squared_error: 0.0283 Epoch 3/48 10/10 [==============================] - 0s 31ms/step - loss: 0.0308 - mean_squared_error: 0.0308 - val_loss: 0.0021 - val_mean_squared_error: 0.0021 Epoch 4/48 10/10 [==============================] - 0s 30ms/step - loss: 0.0096 - mean_squared_error: 0.0096 - val_loss: 0.0022 - val_mean_squared_error: 0.0022 Epoch 5/48 10/10 [==============================] - 0s 34ms/step - loss: 0.0158 - mean_squared_error: 0.0158 - val_loss: 1.2547e-04 - val_mean_squared_error: 1.2547e-04 Epoch 6/48 10/10 [==============================] - 0s 33ms/step - loss: 0.0088 - mean_squared_error: 0.0088 - val_loss: 0.0025 - val_mean_squared_error: 0.0025 Epoch 7/48 10/10 [==============================] - 0s 30ms/step - loss: 7.5392e-04 - mean_squared_error: 7.5392e-04 - val_loss: 0.0412 - val_mean_squared_error: 0.0412 Epoch 8/48 10/10 [==============================] - 0s 32ms/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0017 - val_mean_squared_error: 0.0017 Epoch 9/48 10/10 [==============================] - 0s 46ms/step - loss: 0.0063 - mean_squared_error: 0.0063 - val_loss: 0.0619 - val_mean_squared_error: 0.0619 Epoch 10/48 10/10 [==============================] - 0s 31ms/step - loss: 0.0253 - mean_squared_error: 0.0253 - val_loss: 0.0681 - val_mean_squared_error: 0.0681 Epoch 11/48 10/10 [==============================] - 0s 28ms/step - loss: 0.0206 - mean_squared_error: 0.0206 - val_loss: 0.0032 - val_mean_squared_error: 0.0032 Epoch 12/48 10/10 [==============================] - 0s 30ms/step - loss: 0.0050 - mean_squared_error: 0.0050 - val_loss: 0.0101 - val_mean_squared_error: 0.0101 Epoch 13/48 10/10 [==============================] - 0s 28ms/step - loss: 0.0095 - mean_squared_error: 0.0095 - val_loss: 0.0074 - val_mean_squared_error: 0.0074 Epoch 14/48 10/10 [==============================] - 0s 30ms/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 0.0043 - val_mean_squared_error: 0.0043 Epoch 15/48 10/10 [==============================] - 0s 33ms/step - loss: 0.0025 - mean_squared_error: 0.0025 - val_loss: 0.0016 - val_mean_squared_error: 0.0016 Epoch 16/48 10/10 [==============================] - 0s 32ms/step - loss: 8.0698e-04 - mean_squared_error: 8.0698e-04 - val_loss: 7.1184e-06 - val_mean_squared_error: 7.1184e-06 Epoch 17/48 10/10 [==============================] - 0s 41ms/step - loss: 0.0020 - mean_squared_error: 0.0020 - val_loss: 0.0114 - val_mean_squared_error: 0.0114 Epoch 18/48 10/10 [==============================] - 0s 27ms/step - loss: 0.0094 - mean_squared_error: 0.0094 - val_loss: 0.0563 - val_mean_squared_error: 0.0563 Epoch 19/48 10/10 [==============================] - 0s 36ms/step - loss: 0.0471 - mean_squared_error: 0.0471 - val_loss: 0.0075 - val_mean_squared_error: 0.0075 Epoch 20/48 10/10 [==============================] - 0s 28ms/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0051 - val_mean_squared_error: 0.0051 Epoch 21/48 10/10 [==============================] - 0s 31ms/step - loss: 0.0102 - mean_squared_error: 0.0102 - val_loss: 0.0056 - val_mean_squared_error: 0.0056 Epoch 22/48 10/10 [==============================] - 0s 29ms/step - loss: 0.0066 - mean_squared_error: 0.0066 - val_loss: 0.0215 - val_mean_squared_error: 0.0215 Epoch 23/48 10/10 [==============================] - 0s 28ms/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_mean_squared_error: 0.0045 Epoch 24/48 10/10 [==============================] - 0s 27ms/step - loss: 0.0103 - mean_squared_error: 0.0103 - val_loss: 0.0021 - val_mean_squared_error: 0.0021 Epoch 25/48 10/10 [==============================] - 0s 32ms/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0093 - val_mean_squared_error: 0.0093 Epoch 26/48 10/10 [==============================] - 0s 33ms/step - loss: 0.0069 - mean_squared_error: 0.0069 - val_loss: 1.4736e-04 - val_mean_squared_error: 1.4736e-04 Epoch 27/48 10/10 [==============================] - 0s 27ms/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0116 - val_mean_squared_error: 0.0116 Epoch 28/48 10/10 [==============================] - 0s 29ms/step - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0030 - val_mean_squared_error: 0.0030 Epoch 29/48 10/10 [==============================] - 0s 30ms/step - loss: 0.0086 - mean_squared_error: 0.0086 - val_loss: 2.4833e-04 - val_mean_squared_error: 2.4833e-04 Epoch 30/48 10/10 [==============================] - 0s 33ms/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0349 - val_mean_squared_error: 0.0349 Epoch 31/48 10/10 [==============================] - 0s 42ms/step - loss: 0.0020 - mean_squared_error: 0.0020 - val_loss: 0.0046 - val_mean_squared_error: 0.0046 Epoch 32/48 10/10 [==============================] - 0s 40ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 0.0072 - val_mean_squared_error: 0.0072 Epoch 33/48 10/10 [==============================] - 0s 32ms/step - loss: 0.0091 - mean_squared_error: 0.0091 - val_loss: 0.0019 - val_mean_squared_error: 0.0019 Epoch 34/48 10/10 [==============================] - 0s 37ms/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0110 - val_mean_squared_error: 0.0110 Epoch 35/48 10/10 [==============================] - 1s 53ms/step - loss: 0.0058 - mean_squared_error: 0.0058 - val_loss: 0.0025 - val_mean_squared_error: 0.0025 Epoch 36/48 10/10 [==============================] - 0s 35ms/step - loss: 0.0055 - mean_squared_error: 0.0055 - val_loss: 0.0071 - val_mean_squared_error: 0.0071 Epoch 37/48 10/10 [==============================] - 0s 31ms/step - loss: 0.0019 - mean_squared_error: 0.0019 - val_loss: 0.0034 - val_mean_squared_error: 0.0034 Epoch 38/48 10/10 [==============================] - 0s 38ms/step - loss: 0.0017 - mean_squared_error: 0.0017 - val_loss: 0.0060 - val_mean_squared_error: 0.0060 Epoch 39/48 10/10 [==============================] - 0s 30ms/step - loss: 0.0067 - mean_squared_error: 0.0067 - val_loss: 0.0020 - val_mean_squared_error: 0.0020 Epoch 40/48 10/10 [==============================] - 0s 29ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 1.6743e-04 - val_mean_squared_error: 1.6743e-04 Epoch 41/48 10/10 [==============================] - 0s 30ms/step - loss: 0.0015 - mean_squared_error: 0.0015 - val_loss: 0.0040 - val_mean_squared_error: 0.0040 Epoch 42/48 10/10 [==============================] - 0s 31ms/step - loss: 0.0134 - mean_squared_error: 0.0134 - val_loss: 0.0025 - val_mean_squared_error: 0.0025 Epoch 43/48 10/10 [==============================] - 0s 35ms/step - loss: 5.2350e-04 - mean_squared_error: 5.2350e-04 - val_loss: 0.0109 - val_mean_squared_error: 0.0109 Epoch 44/48 10/10 [==============================] - 0s 41ms/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 1.4305e-04 - val_mean_squared_error: 1.4305e-04 Epoch 45/48 10/10 [==============================] - 0s 31ms/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 5.9630e-04 - val_mean_squared_error: 5.9630e-04 Epoch 46/48 10/10 [==============================] - 0s 32ms/step - loss: 0.0062 - mean_squared_error: 0.0062 - val_loss: 0.0118 - val_mean_squared_error: 0.0118 Epoch 47/48 10/10 [==============================] - 0s 36ms/step - loss: 0.0318 - mean_squared_error: 0.0318 - val_loss: 8.5731e-04 - val_mean_squared_error: 8.5731e-04 Epoch 48/48 10/10 [==============================] - 0s 35ms/step - loss: 0.0112 - mean_squared_error: 0.0112 - val_loss: 0.0326 - val_mean_squared_error: 0.0326
loss_validation_plot(history2,'LSTM multiple layers')
seq_size
9
def calculate_plot_mortality(death_predicted, predicted_confirm):
mortality =[]
for i in range(len(death_predicted)):
mortality.append((death_predicted[i]/predicted_confirm[i])*100)
return mortality
import datetime
base = datetime.datetime.today()
date_list = [base - datetime.timedelta(days=x) for x in range(29)]
print(date_list)
[datetime.datetime(2022, 5, 26, 0, 7, 31, 788208), datetime.datetime(2022, 5, 25, 0, 7, 31, 788208), datetime.datetime(2022, 5, 24, 0, 7, 31, 788208), datetime.datetime(2022, 5, 23, 0, 7, 31, 788208), datetime.datetime(2022, 5, 22, 0, 7, 31, 788208), datetime.datetime(2022, 5, 21, 0, 7, 31, 788208), datetime.datetime(2022, 5, 20, 0, 7, 31, 788208), datetime.datetime(2022, 5, 19, 0, 7, 31, 788208), datetime.datetime(2022, 5, 18, 0, 7, 31, 788208), datetime.datetime(2022, 5, 17, 0, 7, 31, 788208), datetime.datetime(2022, 5, 16, 0, 7, 31, 788208), datetime.datetime(2022, 5, 15, 0, 7, 31, 788208), datetime.datetime(2022, 5, 14, 0, 7, 31, 788208), datetime.datetime(2022, 5, 13, 0, 7, 31, 788208), datetime.datetime(2022, 5, 12, 0, 7, 31, 788208), datetime.datetime(2022, 5, 11, 0, 7, 31, 788208), datetime.datetime(2022, 5, 10, 0, 7, 31, 788208), datetime.datetime(2022, 5, 9, 0, 7, 31, 788208), datetime.datetime(2022, 5, 8, 0, 7, 31, 788208), datetime.datetime(2022, 5, 7, 0, 7, 31, 788208), datetime.datetime(2022, 5, 6, 0, 7, 31, 788208), datetime.datetime(2022, 5, 5, 0, 7, 31, 788208), datetime.datetime(2022, 5, 4, 0, 7, 31, 788208), datetime.datetime(2022, 5, 3, 0, 7, 31, 788208), datetime.datetime(2022, 5, 2, 0, 7, 31, 788208), datetime.datetime(2022, 5, 1, 0, 7, 31, 788208), datetime.datetime(2022, 4, 30, 0, 7, 31, 788208), datetime.datetime(2022, 4, 29, 0, 7, 31, 788208), datetime.datetime(2022, 4, 28, 0, 7, 31, 788208)]
len(death_predicted)
29
mortality_rates = calculate_plot_mortality(death_predicted,predicted_confirmation)
# importing the library
import numpy as np
import matplotlib.pyplot as plt
# data to be plotted
x = np.array(mortality_rates)
y = np.array(date_list)
# plotting
plt.title("Line graph")
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.plot(y, x, color ="green")
plt.show()
#Bidirectional LSTM
# For some sequence forecasting problems we may need LSTM to learn
# sequence in both forward and backward directions
from keras.layers import Bidirectional
print("Bidirectional LSTM")
model3 = Sequential()
model3.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(seq_size, n_features)))
model3.add(Dense(1))
model3.compile(optimizer='adam', loss='mean_squared_error')
model3.summary()
Bidirectional LSTM Model: "sequential_27" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= bidirectional_2 (Bidirection (None, 100) 20800 _________________________________________________________________ dense_27 (Dense) (None, 1) 101 ================================================================= Total params: 20,901 Trainable params: 20,901 Non-trainable params: 0 _________________________________________________________________
bi_model = model3.fit(train_generator,
validation_data=test_generator,
epochs=epoch, steps_per_epoch=20)
Epoch 1/48 20/20 [==============================] - 5s 63ms/step - loss: 0.3552 - val_loss: 0.5157 Epoch 2/48 20/20 [==============================] - 0s 5ms/step - loss: 0.1650 - val_loss: 0.1172 Epoch 3/48 20/20 [==============================] - 0s 6ms/step - loss: 0.0337 - val_loss: 0.0070 Epoch 4/48 20/20 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0019 Epoch 5/48 20/20 [==============================] - 0s 6ms/step - loss: 4.9335e-04 - val_loss: 0.0047 Epoch 6/48 20/20 [==============================] - 0s 6ms/step - loss: 0.0013 - val_loss: 0.0023 Epoch 7/48 20/20 [==============================] - 0s 6ms/step - loss: 6.0919e-04 - val_loss: 0.0044 Epoch 8/48 20/20 [==============================] - 0s 7ms/step - loss: 2.3991e-04 - val_loss: 7.8178e-05 Epoch 9/48 20/20 [==============================] - 0s 7ms/step - loss: 4.8134e-04 - val_loss: 9.5788e-04 Epoch 10/48 20/20 [==============================] - 0s 6ms/step - loss: 2.7100e-04 - val_loss: 0.0031 Epoch 11/48 20/20 [==============================] - 0s 6ms/step - loss: 3.0743e-04 - val_loss: 9.2579e-04 Epoch 12/48 20/20 [==============================] - 0s 5ms/step - loss: 2.4206e-04 - val_loss: 1.8359e-05 Epoch 13/48 20/20 [==============================] - 0s 5ms/step - loss: 2.9365e-04 - val_loss: 1.0265e-04 Epoch 14/48 20/20 [==============================] - 0s 6ms/step - loss: 1.8913e-04 - val_loss: 0.0016 Epoch 15/48 20/20 [==============================] - 0s 5ms/step - loss: 1.4748e-04 - val_loss: 0.0023 Epoch 16/48 20/20 [==============================] - 0s 6ms/step - loss: 2.1346e-04 - val_loss: 8.4822e-05 Epoch 17/48 20/20 [==============================] - 0s 6ms/step - loss: 8.5125e-05 - val_loss: 3.3507e-07 Epoch 18/48 20/20 [==============================] - 0s 6ms/step - loss: 1.5748e-04 - val_loss: 3.7101e-04 Epoch 19/48 20/20 [==============================] - 0s 7ms/step - loss: 1.3914e-04 - val_loss: 6.7378e-04 Epoch 20/48 20/20 [==============================] - 0s 6ms/step - loss: 6.6695e-05 - val_loss: 6.9503e-05 Epoch 21/48 20/20 [==============================] - 0s 7ms/step - loss: 2.0859e-04 - val_loss: 3.1984e-04 Epoch 22/48 20/20 [==============================] - 0s 7ms/step - loss: 1.4012e-04 - val_loss: 0.0013 Epoch 23/48 20/20 [==============================] - 0s 8ms/step - loss: 6.1833e-05 - val_loss: 8.3156e-05 Epoch 24/48 20/20 [==============================] - 0s 7ms/step - loss: 1.2677e-04 - val_loss: 1.0434e-04 Epoch 25/48 20/20 [==============================] - 0s 6ms/step - loss: 2.6335e-04 - val_loss: 7.5116e-04 Epoch 26/48 20/20 [==============================] - 0s 6ms/step - loss: 1.5447e-04 - val_loss: 2.2924e-04 Epoch 27/48 20/20 [==============================] - 0s 6ms/step - loss: 2.1776e-04 - val_loss: 7.3830e-04 Epoch 28/48 20/20 [==============================] - 0s 5ms/step - loss: 7.3249e-05 - val_loss: 8.1568e-06 Epoch 29/48 20/20 [==============================] - 0s 5ms/step - loss: 4.5039e-05 - val_loss: 1.6295e-04 Epoch 30/48 20/20 [==============================] - 0s 7ms/step - loss: 7.7335e-05 - val_loss: 0.0015 Epoch 31/48 20/20 [==============================] - 0s 6ms/step - loss: 9.5322e-05 - val_loss: 7.1508e-05 Epoch 32/48 20/20 [==============================] - 0s 6ms/step - loss: 2.4362e-04 - val_loss: 7.0925e-05 Epoch 33/48 20/20 [==============================] - 0s 6ms/step - loss: 2.2616e-04 - val_loss: 1.6723e-04 Epoch 34/48 20/20 [==============================] - 0s 6ms/step - loss: 2.0494e-04 - val_loss: 1.8036e-04 Epoch 35/48 20/20 [==============================] - 0s 6ms/step - loss: 7.1919e-05 - val_loss: 3.2384e-05 Epoch 36/48 20/20 [==============================] - 0s 7ms/step - loss: 1.7448e-04 - val_loss: 0.0013 Epoch 37/48 20/20 [==============================] - 0s 8ms/step - loss: 2.5130e-04 - val_loss: 1.4444e-05 Epoch 38/48 20/20 [==============================] - 0s 7ms/step - loss: 2.3638e-04 - val_loss: 1.7545e-04 Epoch 39/48 20/20 [==============================] - 0s 7ms/step - loss: 1.4520e-04 - val_loss: 1.6147e-04 Epoch 40/48 20/20 [==============================] - 0s 7ms/step - loss: 6.3992e-05 - val_loss: 6.8745e-04 Epoch 41/48 20/20 [==============================] - 0s 7ms/step - loss: 1.0160e-04 - val_loss: 0.0013 Epoch 42/48 20/20 [==============================] - 0s 7ms/step - loss: 1.8486e-04 - val_loss: 1.8706e-05 Epoch 43/48 20/20 [==============================] - 0s 8ms/step - loss: 1.0671e-04 - val_loss: 6.0568e-06 Epoch 44/48 20/20 [==============================] - 0s 6ms/step - loss: 3.9780e-04 - val_loss: 2.9829e-04 Epoch 45/48 20/20 [==============================] - 0s 6ms/step - loss: 1.1308e-04 - val_loss: 1.4968e-05 Epoch 46/48 20/20 [==============================] - 0s 6ms/step - loss: 8.7012e-05 - val_loss: 1.9861e-04 Epoch 47/48 20/20 [==============================] - 0s 6ms/step - loss: 9.6988e-05 - val_loss: 3.8296e-04 Epoch 48/48 20/20 [==============================] - 0s 6ms/step - loss: 1.8548e-04 - val_loss: 1.2421e-05